{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'google.colab'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-47-35bef2f5146d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# this mounts your Google Drive to the Colab VM.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/content/drive'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# enter the foldername in your Drive where you have saved the unzipped\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'"
     ]
    }
   ],
   "source": [
    "# this mounts your Google Drive to the Colab VM.\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive', force_remount=True)\n",
    "\n",
    "# enter the foldername in your Drive where you have saved the unzipped\n",
    "# assignment folder, e.g. 'cs231n/assignments/assignment3/'\n",
    "FOLDERNAME = None\n",
    "assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n",
    "\n",
    "# now that we've mounted your Drive, this ensures that\n",
    "# the Python interpreter of the Colab VM can load\n",
    "# python files from within it.\n",
    "import sys\n",
    "sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))\n",
    "\n",
    "# this downloads the CIFAR-10 dataset to your Drive\n",
    "# if it doesn't already exist.\n",
    "%cd drive/My\\ Drive/$FOLDERNAME/cs231n/datasets/\n",
    "!bash get_datasets.sh\n",
    "%cd /content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above in [1] may be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [5.32907052e-17 7.04991621e-17 1.85962357e-17]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:   [1.01531428 1.01238373 0.97819988]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "    X = np.random.randn(N, D1)\n",
    "    a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "    batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.7029258328157158e-09\n",
      "dgamma error:  7.420414216247087e-13\n",
      "dbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n",
    "\n",
    "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "\n",
    "we first calculate the mean $\\mu$ and variance $v$.\n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n",
    "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n",
    "\n",
    "\\begin{align}\n",
    "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n",
    "& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/batchnorm_graph.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n",
    "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n",
    "$\\frac{\\partial \\sigma}{\\partial v}$, \n",
    "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n",
    "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n",
    "\n",
    "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  5.34452201909246e-13\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 2.89x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "b1 relative error: 4.44e-08\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 1.01e-10\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 6.96e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "w1 relative error: 1.10e-04\n",
      "w2 relative error: 2.85e-06\n",
      "w3 relative error: 4.05e-10\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "b1 relative error: 2.78e-09\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 1.73e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 8.80e-09\n",
      "gamma2 relative error: 5.28e-09\n",
      "w1 relative error: 1.98e-06\n",
      "w2 relative error: 2.28e-06\n",
      "w3 relative error: 1.11e-08\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "    print('Running check with reg = ', reg)\n",
    "    model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print('Initial loss: ', loss)\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "    if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver with batch norm:\n",
      "(Iteration 1 / 200) loss: 2.684275\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.110000\n",
      "(Epoch 1 / 10) train acc: 0.214000; val_acc: 0.187000\n",
      "(Iteration 21 / 200) loss: 2.043613\n",
      "(Epoch 2 / 10) train acc: 0.328000; val_acc: 0.255000\n",
      "(Iteration 41 / 200) loss: 2.056652\n",
      "(Epoch 3 / 10) train acc: 0.379000; val_acc: 0.283000\n",
      "(Iteration 61 / 200) loss: 1.894575\n",
      "(Epoch 4 / 10) train acc: 0.443000; val_acc: 0.299000\n",
      "(Iteration 81 / 200) loss: 1.497402\n",
      "(Epoch 5 / 10) train acc: 0.452000; val_acc: 0.285000\n",
      "(Iteration 101 / 200) loss: 1.438986\n",
      "(Epoch 6 / 10) train acc: 0.551000; val_acc: 0.321000\n",
      "(Iteration 121 / 200) loss: 1.183682\n",
      "(Epoch 7 / 10) train acc: 0.560000; val_acc: 0.311000\n",
      "(Iteration 141 / 200) loss: 1.510186\n",
      "(Epoch 8 / 10) train acc: 0.624000; val_acc: 0.298000\n",
      "(Iteration 161 / 200) loss: 1.123148\n",
      "(Epoch 9 / 10) train acc: 0.662000; val_acc: 0.330000\n",
      "(Iteration 181 / 200) loss: 1.200461\n",
      "(Epoch 10 / 10) train acc: 0.685000; val_acc: 0.318000\n",
      "\n",
      "Solver without batch norm:\n",
      "(Iteration 1 / 200) loss: 3.016614\n",
      "(Epoch 0 / 10) train acc: 0.102000; val_acc: 0.107000\n",
      "(Epoch 1 / 10) train acc: 0.200000; val_acc: 0.185000\n",
      "(Iteration 21 / 200) loss: 2.352224\n",
      "(Epoch 2 / 10) train acc: 0.256000; val_acc: 0.243000\n",
      "(Iteration 41 / 200) loss: 2.063988\n",
      "(Epoch 3 / 10) train acc: 0.261000; val_acc: 0.223000\n",
      "(Iteration 61 / 200) loss: 1.863201\n",
      "(Epoch 4 / 10) train acc: 0.312000; val_acc: 0.240000\n",
      "(Iteration 81 / 200) loss: 2.215699\n",
      "(Epoch 5 / 10) train acc: 0.367000; val_acc: 0.273000\n",
      "(Iteration 101 / 200) loss: 1.810249\n",
      "(Epoch 6 / 10) train acc: 0.438000; val_acc: 0.284000\n",
      "(Iteration 121 / 200) loss: 1.441531\n",
      "(Epoch 7 / 10) train acc: 0.454000; val_acc: 0.294000\n",
      "(Iteration 141 / 200) loss: 1.491125\n",
      "(Epoch 8 / 10) train acc: 0.465000; val_acc: 0.304000\n",
      "(Iteration 161 / 200) loss: 1.427026\n",
      "(Epoch 9 / 10) train acc: 0.514000; val_acc: 0.320000\n",
      "(Iteration 181 / 200) loss: 1.453083\n",
      "(Epoch 10 / 10) train acc: 0.501000; val_acc: 0.281000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "print('Solver with batch norm:')\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "print('\\nSolver without batch norm:')\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight[1.00000000e-04 1.62377674e-04 2.63665090e-04 4.28133240e-04\n",
      " 6.95192796e-04 1.12883789e-03 1.83298071e-03 2.97635144e-03\n",
      " 4.83293024e-03 7.84759970e-03 1.27427499e-02 2.06913808e-02\n",
      " 3.35981829e-02 5.45559478e-02 8.85866790e-02 1.43844989e-01\n",
      " 2.33572147e-01 3.79269019e-01 6.15848211e-01 1.00000000e+00]\n",
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "print(\"weight{}\".format(weight_scales))\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "    print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "    bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    bn_solver.train()\n",
    "    bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    solver.train()\n",
    "    solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EJnrhMB4XDl+P1HMwxhhjjKm09uyCZVO8UPg57FwPEg/Njocug6FVb6jdfO/xPe7fv5tpYgr0uA+q13W35n/Zu08Vdm6w4GhMBSNB5nepdLp27arTp9uqGMYYY4yJcTvWw+8TXShcMhnysiG5Jhze07UStuwJKbWKP3/2cPjqIdeSmNrYhcOyTlBTXHBcvwCyN+897s/g2NoFxrpegKzRoPjgGI76jKmkRGSGqnYt9TgLiMYYY4wxlZSqC1+FXUdX/QKo6xbauo8LhU2Ph4QkvysNPTgmp3phsUhwXP598BbOvs9bSDQGC4j7sIBojDHGmJiRnwcrf947yczmpW57w84uELbuA/U6VJzum6EGR4QgE+a7MHzL3PKq1pioFWpAjOQkNcYYY4wxpjzs3g4ZX7lA+PtEyNkK8UnQ/CTofqMbT5jayO8qD4xIaGMcJ9we/PxgE+sYY4plAdEYY4wxpiLalrm36+jy7yB/D6TU3tt1tMUpkFzD7yojp2hw/PF52LZy/+NSG5d/bcZUYBYQjTHGGGMqAlVYO3tv19E1v7nttVtAt+tcKGx8DMTH6Me7HvftPwYRoPOl/tRjTAUVo39BjDHGGGOiTLAZONv1d5OvLJrgblmrAIEm3aDngy4Uprfyu/LoUDgRTeHvsEZ9yM+FH19wE/E0P9Hf+oypIGySGmOMMcYYv80evn/rl8RDXALk74bEqtDiVG8pitOherp/tVYkWWvgvbNhy3IY8C606uV3Rcb4xiapMcYYY4yJdtlbXbfRz/65f9dIzYf4KnDhe26MXWKKPzVWZDUbwOWfwdBzYdhAOPc16HCe31UZE9UsIBpjjDHGlIcd62HNbFgzy40fXDvbtWyVZM8ua/U6WNUOgcGfwgcXwoirYPcO6DLY76qMiVoWEI0xxhhjwkkVsjJdCAy8bV+z95hazaHBkXDUYGhwBIy9EbJW738tm4EzPKrUhEs/geGXua68e3ZA9xv8rsqYqGQB0RhjjDHmQBUUwJZl+4fBwgXcJQ7qtHJdRBsc4W71O0KV1H2v0/PB/ccgJqa4iWpMeCRVhYs+hE+ugon/dmtHnnSHWy7DGPMnC4jGGGOMMaHIz4ONv7uuoX+GwdmwZ7vbH5cI9dpBmzO9MHgk1Gvvgklpis7AWTiLaeF2Ex4JSXD+Wy6Mf/O4C4mnP2Ih0ZgAFhCNMcYYY4rK2w3r5+8NgWt+g3VzIS/H7U9IcS2BR1y4t2Uwva0LIAeq0wALhOUhPgH6vQhJ1eGnF2F3Fpz1HMTF+12ZMVHBAqIxxhhjYkOwdQY7DYA9O2HtXG/iGK9lcP0CKMhz5yXXdAGw61V7w2CdlhYoKrK4OOjzX0iuAd895SauOfc1iE/0uzJjfGcB0RhjjDGVX9F1BrethFHXwxf3wo51gLcudNVDXNfQ406DBp1cGExr5gKFqVxEoMe9LiROuh9yd8EFb9tyIibmWUA0FU9x3wAbY4wxhXZudK2AGxa626/vuQXnA2k+5GyFk++E+l4YrNnQxqPFmhNudiFx/G3w/gVw8YfuvjExygKiqViCfQP86U3uZwuJxhgTe4oGwfULYcMC2LVp7zHJNfcPh4XydruAaGLb0Ve5UDjqenj3bLjkY6ha2++qjPGFBUSzv0i20Km6Af67t7tbzra9P/95K7ItJ2vvz+vnu298A+Vmu3otIBpjTOUVahBMbwOtz4C6bd3P6W1cq+BzHd2XikXZOoOmUKcBkFQNPr4c3ukLl42C6nX9rsqYcmcB0eyrpBa6tn0DwlpAaNsdZFtgqPtzn7etcNB/SeKT3Td5yTXc4rbJNSGtCaybE/z4bavC8/yNMcb4a8eGgBC4ADYsKlsQLK57aI/7bJ1BU7o2Z8LA4TBsIAzpDYPGuM8fxsQQUdXIXVykN/A/IB54Q1X/U2T/9cANQD6wA7hWVed7++4CrvL23aSqE0O5ZjBdu3bV6dOnh+15VWrPdgj+DWuo4pO9QOeFu+SaRf4NuFVJ3X9bciokV4eE5LLVV6MB3LbwwOs2xhhz8MrSA6UsQTC9dehBMFz1+WD0zEyenLiI1VuzaZiWwu29WnN250Z+lxWb/pjqxiMm14DBY+GQFn5XZMxBE5EZqtq11OMiFRBFJB74HTgNWAX8AlxcGAC9Y2qqapb3cz/gb6raW0TaAR8CxwANgUlAK++0Eq8ZjAXEMnggjT9nciuq5wNBwl5gACwh2IVL0RbOQsk14YoJUL9DZB/fGGNMcMH+PiemwGmPQnqr8g2CFdDomZncNXIO2bl7h1GkJMbz+LkdLST6Zc1v8N45IPGuu6l9xjAVXKgBMZJdTI8BMlR1qVfQMKA/8GeYKwyHnmrsTSb9gWGquhtYJiIZ3vUo7ZrmIMwbXfy+1CZwwi3lV0txCr/pDfwG+OirYOpr8NYZMPAjaNrd3xqNMSYWffXQ/l/e5WbDZ7fuvX8gXUNjxJMTF+0TDgGyc/N5cuIiC4h+aXAEXPE5vNsf3j4DLh0JjUv9bG1MhRfJgNgICOwLuAroVvQgEbkBuBVIAk4NOPfnIucW/nUs9Zreda8FrgU49NBDy159LMnPc+v//PQipDWHHWvcRDKFom2MRqcB+3cJ6nCe+5bvvbPdGkat+/hSmjHGxJSs1bD8e1j+XcnDEy4daUGwFKu3Zgfdnrk1mx8yNtKteW0S4m0txnKX3gqu9ELiO/1g4DBo/he/qzImoiL5lybY/wH267uoqi+pagvgDuCeUs4N6ZredV9T1a6q2jU9PT3EkmPQ9nXwbj8XDo++Gm6cCv1ecC2GiPu37/NRNUYjqLRD4cqJ7hvpYZfArA/8rsgYYyqfrNWuK+nYv8PzneGZtjDyGpg3BhKqBD8ntQkc3gNSG1k4LEHDtOCLswtwyRtT6fbYV/x71Bx+yNhIXn5B+RYX62o1dSEx7VAYej4s+tzvioyJqEi2IK4CAqd9agysLuH4YcDLIZxblmuakqz4yU3lnLMNznkNjrjQbQ/WQlcRVKsDgz91AXH0X934luP+7ndVxhhTcQW2EC7/HjYvdduTU6HpcdD1Kmh2AtTvCHM/sVlCD8Jtp7Xkto9n7/Otd0piPA/2a0+NKgmMm7OGUb9m8sHUPzikWhK9OtTnzI4NrGWxvNSoD1d8BkPPhY8ugXNfc72XjKmEIhkQfwFaikhzIBO4CBgYeICItFTVxd7dM4HCn8cCH4jIM7hJaloC03BfpJV4TRMCVfj5ZfjyXvdt2GUjoV57v6sKj+QabnHbkdfAF/e4dbN6PmDfWhtjTCjKEgjj4vc9N9gY8SibJTSabd6ViwK1qyayZVfufrOY9unYgOw9+XyzaL2FRb9UrQ2DxsKHF8GIq2D3Dugy2O+qjAm7SC9zcQbwHG5JiiGq+qiIPARMV9WxIvI/oCeQC2wBblTVed65dwNXAnnAzao6obhrllaHzWIaYPcO1zVo3khofSac87JbbqKyKciHz/4J04dA50vhrP9BvC37aYwx+ygtEDY7ofhAaMImc2s2pz0zhWMPO4Q3B3dFQvhSMzAsfr1gPdm5+RYWy8ueXTD8MsiYBL0eg+43+F2RMSHxfZmLaGIB0bPhd/joUti0GE69F46/GeIq8f88VOGbx2HKf6HNWXDem5BYzBgZY4yJBRYIo9I1707nu8Ub+PKWk2hSu2qZz7ew6IO8PTDyapg/Bk6+C066w3ormahnATGABUTcEhZjbnCTCJz/Jhx2st8VlZ+pr8KEf0HTE+DiDypni6kxxgRjgTDqfTFvLde+N4M7+7Th+pMOfjF2C4vlKD/Pjbud9T50vxFOf8RCoolqFhADxHRADFzColFXGPCum0ku1swZAaOuc7OcXjoSqtf1uyJjjCm72cNLHuNngbBC2bk7j9OemUKNKomMu+kEEsMc3ArD4vg5a/iqSFg8q2MDjrGwePAKCmDiXTD1FThqEJz1nL23TNSygBggZgPi9nUw4gpY8QMcfY3rJ5+Q5HdV/lk8yY0ZqF4PLhsFtZv7XZExxoRu9vD9ZwlNqAJHXgIFuRYIK6BHx8/n9e+W8clfu9Olae2IPpaFxQhShcmPwrdPQvtz3Qyn8Yl+V2XMfiwgBojJgBi4hEW/CrCOYXlZ+Qt8cAHEJ7mWxPod/K7IGGNKlp8L29fA6z1g5/rgx1ggrHDmr86i74vfM6BrYx4/t1O5PraFxQj5/jnXa6tVb7jgbbfMizFRxAJigJgKiEWXsLhwaOVZwiJc1i+E986BPTth4EfQtLvfFRljYlVh+Mta7bqNZq2GrEx325bp7u9YB5T0/2qB+zZZIKxA8guU817+kZWbd/HVbSeRVtW/3j0WFsPslzdh/G3ui5qLP3TLbxkTJSwgBoiZgBgrS1iEw9aVLiRuW+m+5Wvdx++KjDGVzYGGv6Qabqx4zYZQs5F3awhfP+TWdi0qtQncMrdcnpIJj/d+XsG9o+fy7IVHcE7nxn6X86dQwuK42Wt4cuIiVm/N3m+tRuOZ/bGb96BhZ7c2c9XIdh82JlQWEAPERECMtSUswmHnRnj/fFgzG/q/CEcO9LsiA6VPwmFMJIX6+gt3+EtttPd+lZrF11Z0DGJiCvS1YQQVyfrtOfR4egqdGqcy9KpuIa156IdgYbFaUjw5eQXkF+x9XackxvP4uR0tJBa1cLwb6nNISzfvQY16fldkjAXEQJU+IMbyEhYHa/d2GHYJLJvipqc+7u9+VxTb7AOw8VOw1198ErTtB9XqeAFwtQuAoYa/oveLC39lqdG+QKnQ/v7hTCbOXcvnN5/IYenV/S4nJIVh8dbhs8jOLdhvf6O0FH6481QfKotySybDsIFQowEMGgNpTfyuyMQ4C4gBKm1ADFzCovHRcME7sbmExcHK2w0jr4X5o13La88HbB0jvzzTzn0IL6pKqhtPW78TpKSVf10mNjzbwXU7D6Y8wp+p9L79fQODhkzj5p4tublnK7/LKbPmd44POhpWgGX/ObO8y6kY/pgK71/gxiIOGgN1Dve7IhPDQg2ICeVRjIkAW8IifBKS4fwh8Flt+OE52LURzvofxNvbo9xsWQ4/vxI8HIKbjfedvu7ntKbQ4AjvdiQ06GTrWprw2LaqmB0C/y5unzGhycnN594xczmsTjX+enILv8s5IA3TUsjcmh10uynGod3g8nFu3oO3esOxf4PpQ6wXgIlq9gm4IgpcwuLc1+0PSzjExcOZz0C1dJjyX8jeCue9CYlV/K6s8lKFldNcC/jCcSBxkFgVcnftf2zNhtDvBTdedM1v7rZg7N79NRrsDY31O7l/UxvHZkuwdUE8MHM/KX5favRMImIqrpcmZ7Bi0y4+uKYbyQkVc8bZ23u15q6Rc8jOzf9zW3JCHLf3au1jVRVAg05wxQR48zT46sG927etdN3awf5Om6hiAbEiKbqExWUjbQmLcBKBU/4NVevAhH/B0PPg4g9sJthwy8+DBWPgp5cgcwZUSYPj/wHHXOsW+g42BrHng3B4T3crlLMN1s7ZGxjX/AaLvwD1xsek1A5oaezkWhtrNa/ckzcVHUNnHz5Kl58LX9wLU1+G2i1cK3Zezt79iSkuZBtzEDLWb+eVKUs4t3MjjmtRx+9yDljhRDSFs5gCtEivZhPUhCK9lft7krN13+252e5LPfsbbaKIjUGsKGwJi/I1Z4SborpuW7h0pHVhDIecbfDruzD1VRdcah/mutocORCSqu097mBawPbsgnXzYM0sFxjXzoZ186Eg1+1PquHCYmErY4MjoE6ritmdOD8Psje72Xh3bnBdo8fduv+HD7BlEIqTtcb1xlj5M3T7K5z+MMwbZS2wJqxUlYte+5mFa7fz1W0nUad6st8lhc1r3y7hsc8W8tYVR3NKa/v/ZKkeSCP4mqYCDwT5221MmNkkNQEqfEC0JSz8kTEJProMqtdzU1TXbu53RRVT4fjCme/Bnh3Q9ATofgO06l0+r+O8PbBhQUBL42zX8pjntbIlVHEt8X+2Nh4Bddu5sanBRKoLZ7DAt3NTwM/erfDn7C2UvHh6IPvwsZ/l38PHV8CendDveeh4vt8VmUrq4+kruX3EbB4/tyMXH3Oo3+WE1Z68Ano99y0i8Pk//kJSgn02KVGxE2GJm0W9+w1Qo36/0wviAAAgAElEQVS5l2VihwXEABU6INoSFv5a+Qt8cIGb6v7SkVC/g98VVQzBxhd2OM+1GDY80u/qoCAfNi7e28pYGB53Z7n9cQmQ3nbf0Fi/g1vXKtRlOP4MfBv2DXZ//rzBBcDCn7O3Uuw3y1Vru/GxVetAtUMCfvZuhT8PPdctw1BUUjW4daHNsgnutfnjCzDpAdeKfeFQqNvG76pMJbV55x56PP0NLdKrM/y67sTFVb5x0V8vXMeVb0/nnjPbcvWJh/ldTnQLtpROQjLUPwIyp7v/9xw50A27qG2/SxN+FhADVMiAaEtYRI/1C90H7907YOAwaHqc3xVFr2DjC7te4cYX1mzod3UlKyiArcv3nQhnzW8uwAEgbjKjgrz9z02qBi1ODX/gq5YOKbXc44Yi2IePuARXc2oT11LWIobXKsvJcl+4LRjr1jbs/5KFZhNRt3/8G6NmZjL+phNpXb+G3+VEzOVvTWPG8i1Mvv3kStWFNiKK64WyeSn88DzMet/9zW5/DpxwC9Tv6HfFphKxgBigwgVEW8Ii+mxd6aao3rYSLngbWvfxu6LoEur4wopGFbav2RsWv3m8+GPT24Q/8B2IYB8+ajWH0X913dS7XA6nPRx7wWj9AtdlfPNSOO1B6H5jbM5ya8rN1KWbuPC1n7n+pBbc2adyt1Iv2bCDXs9+y/ldGvOf8zr5XU7Ftn2t+5J1+hA3LKPl6XDCrdC0u9+VmUrAAmKAChUQA5ew6Bek25rxz86N8P75roWp/4su/MQ6v8cXlrfixo9UhElgcrNh8mOuV0LNRrHVmjj3Exjzd/dlxQVvQbMT/K7IVHJ78go44/nvyMnN58tbTiIlqWIua1EWj4ybz5s/LGPsDSfQsbFNonfQsrfAL2+42et3bYJDu7ug2PI0+3LLHLBQA2Il/ARXQanCT/8H75wFSVXhmq8sHEabanVg8KfQ/ETXGvPjC35X5A9V+GOqa415vjP88jq0OROunQJXjIc2Z1TOcAiuNS6xyILQFWUZhMQUN0vnlV+4Mc3vnQOf/sN1u6ys8vbAhDthxJWum9Z131o4NOXitW+XkLF+Bw/37xAT4RDgpp4tOaRaEg9+Oo9YaHyIuJRa8Jfb4ea50OcJ1yvkgwvglRPcTOv5QYY7GBMmEf0UJyK9RWSRiGSIyJ1B9t8qIvNFZLaIfCUiTb3tp4jIrIBbjoic7e17W0SWBeyLghkvDtLuHe4DzMS7oGUvuPYbW98wWiXXgIHDod3Z8MU98OX9LjDFgvw81xLzRg8Ycjos+9YNpL95Dpz7WnRMPhNpnQa4CWlSmwDi/g02QU00a3I0XP8dHHeT6xb88nGw5Gu/qwq/rDXuC7epL7vuzpePg5oN/K7KxIAVm3bywtcZnNGxPqe0iZ2lH2pWSeT2Xq2ZvmILY38LMlmWOTBJVaHbdXDTTDj7Zbd26ydXwYtdXDfU3JzSr2FMGUWsi6mIxAO/A6cBq4BfgItVdX7AMacAU1V1l4j8FThZVS8scp3aQAbQ2DvubWCcqo4ItZao62IaOEaoej23bed6W8KiIinIh8/+6f44d74UzvpfxVxLLxSVdXyhcbP0VsaxiYFLWPR/wc2ga0w5UFUGv/ULv67Ywle3nUS9mlX8Lqlc5Rco/V/6no3b9/D1P0+ialIl/f+inwoKYNF4+O4ZWP2r+xzZ/QboeqX7EtuYEkRDF9NjgAxVXaqqe4BhQP/AA1R1sqru8u7+DDQOcp3zgQkBx1VshbMMblsJKOxY627H3wwn3mrhsKKIi4czn4GT7oSZQ+HjwZXvW7wty133vGfaudbSWs3g4mFw4ww45hoLh5VBZWtNVHWzAL7TD6qkwjVfWzg05Wrc7DV8+/sG/nl6q5gLhwDxccIDfduzNiuHV75Z4nc5lVNcHLTt6/6+DRoDddvCl/fBs+3h60fcfAnGHKRIppFGQOBsDqu8bcW5CpgQZPtFwIdFtj3qdUt9VkSCzqcsIteKyHQRmb5hw4ay1B1ZXz207xT0heZ8XP61mIMjAqfcBX2edOvjDT3PtbZVZCWNL7x8nJu91b7EqFwqy9jEnCwYPgi+vNe9Zq/52tY3NOVqW3YuD42bT8dGqVzWvZnf5fima7Pa9D+yIa9+u5SVmyvHd/tRScStjT1ojPt71/wv8O1TbjK1CXe42deNOUCR/KQXbIqloP1ZReRSoCvwZJHtDYCOwMSAzXcBbYCjgdrAHcGuqaqvqWpXVe2anp5e9uojZduqsm030a/btXDeG7DyZ3j7TJj2hvsD/UCa+3f2cL8r3Nfs4fvXF3R84c2xNb4w1lXk1sT1C+D1U90XNac/AgPerRxdZU2F8tTERWzasZvHzulIfFxszzJ5Z582xInw2GcL/C4lNjTqAhcOhRumQodz3eynzx8Jo/4KGxb5XZ2pgCIZEFcBTQLuNwb2G7UsIj2Bu4F+qrq7yO4BwChVzS3coKpr1NkNvIXrylpxpAbrRVvCdlMxdDwfBn4E6xe6sYmFXYi3rXRdiqMlJBbt4rxtJYz+GzzV0k2UlL0FzngKbp0PPe+P/sXtTXhVxNbEOSNcOMzZBoPHwnF/tyngTbmbtXIrQ6euYFD3ZrbEA9AgNYW/ndyCCXPX8uMS6/JYbtJbw9n/BzfNgqOvhnmj4KVuMOwSyJzhd3WmAonkJDUJuElqegCZuElqBqrqvIBjOgMjgN6qujjINX4G7lLVyQHbGqjqGhER4FkgR1X3myE1UFRNUlP4AT2wm2liSsWbCdEE91RL2LF+/+1V67g/2n4b/TfYFeR/1vHJMOAdN4uudSE1EP3rJubtcd1Jp74CTY6FC962WUqNL/LyC+j34g9s2rmbSbeeRI0qiX6XFBVycvPp+cwUqicnMO7vJ5AQb/9vKXc7N7q/kdNec1+iNT8JTrjFdU21L9JiUqiT1EQsIHpFnAE8B8QDQ1T1URF5CJiuqmNFZBKuC+ka75Q/VLWfd24z4AegiaoWBFzzayAd14V1FnC9qu4oqY6oCoiw7yymqY3dGmoWDiuHB9Iopid1lBN4YKvfRZhoFI0znWatcRNDrZzqZtQ97SGItw/lxh9vfLeUR8Yv4OVLjqJPR/uSItCEOWv46/u/8nD/9jE9LtN3OVkw4y346SXYsQ4aHuWCYpuz7EvhGBMVATFaRF1ANJXXsx287ptFVK/rZgD124cXBW/hTG0Ct8wt/3pMxRBNrYm2hIWJIplbszntmSkce9ghvDm4K2KtMvtQVQa+PpUFa7P45p8nk1Y1ye+SYltuDvz2IfzwP9iyDOq0cvMNdBpgX7LFCAuIASwgmnIT7V2Io70+E938bE1UhR9fgEkPuLU4Lxxqs5Qa313z7nS+W7yBL285iSa1q/pdTlRauDaLM/73HZcd25QH+3fwuxwDbmK6+aPh+2dh3Vyo2diN3z5qECwcZ73cKrFoWAfRmNjTaYALW6lNAHH/RlP4ivb6THTza6bTnCwYfpktYWGiyhfz1vLl/HXc3LOVhcMStKlfk0u6NWXo1D9YtHa73+UYgPgEN7ne9d/DwI8hrQl8fgc8ebibqyBaJ9oz5cZaEI0xxpRdYGviUYPd8hKRaE1cvwA+uhQ2L4PTHoTuN9rkCsZ3O3fncdozU6iZksinfz+BRJuApURbdu7h5Ke+oX3Dmrx/dTfrihuNVvwE750NeTn777NhKJWGtSAaY4yJnMDWxJnvwf91h4yvwvsYfy5hkWVLWJio8tyk31m9LYdHz+lg4TAEtaolcdvprfhxySYmzlvndzkmmKbdIa/oanMeW6s75thfNWOMMQcmcN3ExBQYei6Mveng103M2wMT7oBProL6neC6b6HZCeGp2ZiDNH91FkN+WM7FxxxKl6a1/S6nwhh4zKG0rleDRz+bT05uvt/lmGCKW5O7hs3OG2tKDYgiEl8ehRhjjKmgwtmamLUG3jnLrd117N/g8nG2vqGJGvkFyr9HzSEtJZE7e9s42LJIiI/jvr7tWLk5mze/X+Z3OSaYHve5L/uK2rMTVs0o/3qMb0JpQcwQkSdFpF3EqzHGGFMxhaM1cdl38OqJsHYunD8Eej9uU6+bqPLBtD+YtXIr95zVltSq9tosq+MPr0Ov9vV4aXIGa7cFGetm/BVsIrse90FKKrzVG2YO9btCU05KnaRGRGoAFwFX4ALlEGCYqh5kH6LyY5PUGGNMOQpcN7FGQ7du4uE9ij9eFX58HiY9aEtYmKi1fnsOPZ6eQqfGqQy9yiZaOVArN++ixzNTOKNDfZ67qLPf5ZhQ7NwEIy6HZd/CMddBr0fty7sKKmyT1KjqdlV9XVWPA/4F3A+sEZF3ROTwMNRqjDGmMilLa+KfS1jcZ0tYmKj28LgF7M4t4OH+HSwcHoQmtaty7YmHMXrWamas2Ox3OSYU1Q6BS0fBsTfAtFfh3bNh50a/qzIRFEoLYjxwJq4FsRnwHvA+cCLwmKq2inCNB81aEI0xxidFWxM7ng9zP3Gz4lWvB1oAuzbZEhYmqn37+wYGDZnGzT1bcnPPqP/YE/V27s7j1Ke/oV7NKoz+2/HExdn7vsL4bZj7wq96XbjofWhwhN8VmTII5zIXi4H+wJOq2llVn1HVdao6Avj8YAs1xhhTiQW2Jubnwg/P7V2Eecda2LkeTrjFlrAwUSsnN597x8zlsDrV+OvJLfwup1KolpzAXX3aMnvVNkbMsCUUKpQjLoIrP3df7r3ZC2Z/7HdFJgJCCYidVPUqVf2x6A5VvSkCNRljjKlsmhxd/JiV2R+Vby3GlMFLkzNYsWkXj5zTgeQEm9g9XPof2ZAuTWvxxMSFbM/J9bscUxaNjoJrv4GGR8LIq+GLeyA/z++qTBiFEhBfEpG0wjsiUktEhkSwJmOMMZVRVmbw7bYIs4lSGeu388qUJZzbuRHHtajjdzmViohwf992bNq5hxe+zvC7HFNW1evCoLFw9NXw4wvw/vmwy8aUVhahtiBuLbyjqlsAm3bKGGNM2RS3CHNx243xkapy96i5VE1K4N9ntvW7nEqpU+M0LujSmLd+WMbSDTv8LseUVUISnPm0Wxpj+ffw+imwbp7fVZkwCCUgxolIrcI7IlIbSIhcScYYYyqlYIswJ6a47cZEmREzVjF12Wbu6tOGOtWT/S6n0rq9VxuSE+J5ZPwCv0sxB6rLYLjiM8jNgTdOg/lj/K7IHKRQAuLTwI8i8rCIPAz8CDwR2bKMMcZUOsEWYe77vNtuTBTZvHMPj322gK5NazGgaxO/y6nU0mskc1OPw/l64XomL1rvdznmQDU5xo1LrNsWhg+Crx6GggK/qzIHqNRlLgBEpD1wCiDAV6o6P9KFhZMtc2GMMcaYUN3+8W+MmpnJ+JtOpHX9Gn6XU+ntySug93PfAvD5zX8hKSGU9gsTlfJ2w/hbYeZQaNkLznsdqqT6XZXxhHOZC1R1HjAcGAPsEJFDD7I+Y4wxxpioM3XpJj6esYqrTzzMwmE5SUqI496z2rF0407e/Wm53+WYg5GQDP1ehDOegiVfweunwobf/a7KlFGpAVFE+onIYmAZMAVYDkwI5eIi0ltEFolIhojcGWT/rSIyX0Rmi8hXItI0YF++iMzybmMDtjcXkakislhEPhKRpFBqMcYYY4wpyZ68Au4ePZfGtVL4R4+WfpcTU05pU5eTW6fzv0mL2bB9t9/lmIMhAsdcA4PGQPZWFxIXhRQdTJQIpQXxYeBY4HdVbQ70AH4o7SQRiQdeAvoA7YCLRaRdkcNmAl1VtRMwgn3HNmar6pHerV/A9v8Cz6pqS2ALcFUIz8EYY4wxpkSvfbuEjPU7eLh/B1KSbM3D8nbvWe3Izs3nqYmL/C7FhEOzE9y4xEMOgw8vgilP2LjECiKUgJirqptws5nGqepk4MgQzjsGyFDVpaq6BxgG9A88QFUnq+ou7+7PQIlznYuIAKfiwiTAO8DZIdRijDHGGFOsFZt28sLXGZzRsT6ntKnrdzkxqUV6da44vhnDZ6xkzqptfpdjwiGtCVw5ETpdCJMfhY8Hwe7tfldlShFKQNwqItWBb4H3ReR/QF4I5zUCVgbcX+VtK85V7Nt1tYqITBeRn0WkMAQeAmxV1cLHL+2axhhjjDElUlXuHTOPxPg47u/b3u9yYtrfe7TkkGpJPPjpPEKZSNFUAIkpcM6r0OsxWDjeLYWxaYnfVZkShBIQ+wO7gFuAz4ElQN8QzpMg24K+00XkUqAr8GTA5kO9WXYGAs+JSIsyXvNaL2BO37BhQwjlGmOMMSYWjZu9hm9/38A/T29FvZpV/C4nptWsksjtvVozfcUWxv622u9yTLiIQPcb4NKRsGMtvH4KZEzyuypTjBIDojeOcIyqFqhqnqq+o6rPe11OS7MKCFw8qDGw3ztdRHoCdwP9VPXPUcmqutr7dynwDdAZ2AikiUhCSdf0zntNVbuqatf09PQQyjXGGGNMrNmWnctD4+bTqXEql3Vv5nc5BrigSxM6Nkrl8c8WsmtPKJ3WTIXR4hS4ZjLUbAzvXwDfPwfWUhx1SgyIqpoP7BKRA1nA5BegpTfraBJwETA28AAR6Qy8iguH6wO21xKRZO/nOsDxwHx1fQ0mA+d7hw7GLb1hjDHGGFNmT01cxKYdu3nsnI7ExwXrqGTKW1yccH/fdqzNyuHlb6wrYqVTuzlc/SW07QeT7odProI9u0o/z5SbULqY5gBzRORNEXm+8FbaSd44wRuBicACYLiqzhORh0SkcFbSJ4HqwMdFlrNoC0wXkd9wgfA/qjrf23cHcKuIZODGJL4Z4nM1xhhjjPnTrJVbGTp1BYOPa0aHRraYdzTp2qw2/Y9syKvfLmXlZgsPlU5SNbjgbehxP8wdCUNOhy0r/K7KeKS0AcAiMjjYdlV9JyIVRUDXrl11+vTpfpdhjDHGmCiRl19Avxd/YPPOPUy67SSqJyeUfpIpV2u2ZXPqU1M4uXU6L1/axe9yTKQs/hJGXAVx8S40HnaS3xVVWiIyw5vjpUSltiB64w73u4WnTGOMMcaY8vf2j8uZvyaL+/u2s3AYpRqkpvC3k1swYe5aflyy0e9yTKS0PA2unQzV0uG9c+Dnl21cos9CaUFcRpCZQlX1sEgVFW7WgmiMMcaY0TMzeXLiIlZvzQagbYMajL/pRNwyyyYa5eTm0/OZKVRLSmD8TSeQEB/K6ChTIeVkwajrYdF4OOJiOOtZt0SGCZuwtSDilp842rudCDwPDD248owxxhhjys/omZncNXIOmVuzUdw330s27GTMLFtKIZpVSYzn7jPasmjddj6c9off5ZhIqlITLhwKJ98Fv30Ib/WBbav8riomhdLFdFPALVNVnwNOLYfajDHGGGMOWFZOLjNWbGHYtD+4e/QcsnPz99m/O6+AJycu8qk6E6reHerT/bBDePrL39m6a4/f5ZhIiouDk++Eiz6AjYvhtZNhxU9+VxVzSu10LyJHBdyNw7Uo1ohYRcYYY4wxZZCVk8vidTtYvG47v6/bweL121m8bgdrs3JKPbewu6mJXiLC/f3accb/vuOZL3/nof4d/C7JRFqbM+Hqr2DYxfDOWdDnCTj6Kr+rihmhjMp+OuDnPGAZMCAy5RhjjDHGBOeCoAt/xQXBlMR4Dq9bneMOP4SWdWvQql51WtWrwYWv/cTqrfsHxoZpNsapImhTvyaXdGvK0J9XMLDbobSpX9Pvkkyk1W0D10yGT66G8bfC2tnQpBtMfsx1PU1tDD3ug04WS8Kt1ElqKgObpMYYY4ypOMoSBFvWq75PEGyUlkJckAXvC8cgBnYzTUmM5/FzO3J250bl8rzMwdmycw8nP/UN7RvW5P2ru9nkQrGiIB++fgS+fwYkDrRg777EFOj7vIXEEIU6SU0oXUwfA55Q1a3e/VrAbap6z8GXaYwxxpjKInCW0IZpKdzeq3WJ4Wtbdi4Z60NvEWxVrwYt65YcBItTWEdZ6jPRpVa1JG47vRX3jZnHxHlr6d2hgd8lmfIQFw8974df34Fdm/bdl5sNXz1kATHMQlnmYqaqdi6y7VdVPaq4c6KNtSAaY4wxkVVSC90pbeqGHARbei2BBxoETeWWl1/Amc9/z849eUy69SSqJMb7XZIpLw+kEWTlPUDgga3lXU2FFLYWRCBeRJJVdbd34RQg+WALNMYYY0zl8eTERfvNEpqdm8+tw2dREPCZLlwtgiY2JcTHcX/fdgx8YypvfLeUG09t6XdJprykNoZtK4NvN2EVSkAcCnwlIm/hYvuVwDsRrcoYY4wxFUpxs4EWKNzZp40FQRM2xx1eh97t6/PS5CWc16UxDVJtoqGY0OM++PQm16000FGD/KmnEgtlHcQngEeAtkB74GFvmzHGGGMMAA1SqwTd3igthetPakGPtvVoUruqhUMTFnef2ZZ8Vf47YaHfpZjy0mmAm5AmtQkgUKMBpNSGn16E1TP9rq5SCWWSmubAN6r6uXc/RUSaqerySBdnjDHGmOi3J6+AWtWSWL1t32UkUhLjub1Xa5+qMpVZk9pVufbEw3hxcgaXdW9Kl6a1/S7JlIdOA/adkGbLCrdO4rv9YdAYaNi5+HNNyEptQQQ+BgLmkyXf22aMMcaYGJeXX8A/hs1k3uoszjuqEY3SUhBcy6EtIWEi6W+ntKB+zSo8MHY+BQWVf9k2E0StpjB4HFRJdSHRWhLDIpQxiAmquqfwjqruEZGkCNZkjDHGmAogv0C5dfhvTJi7lnvObMvVJx7md0kmhlRNSuDOPm24+aNZjJixigFHN/G7JOOHwpBoLYlhE0oL4gYR6Vd4R0T6AxsjV5Ixxhhjol1BgXLHJ7MZ+9tq/tW7tYVD44v+RzakS9NaPDFxIVk5uX6XY/xiLYlhFUpAvB74t4j8ISIrgTuA6yJbljHGGGOilapy9+i5jJixilt6tuJvJx/ud0kmRokID/Rtz6ade3jhq8V+l2P8ZCExbEKZxXSJqh4LtAPaqepxqpoR+dKMMcYYE21UlQfGzuPDaX9wwyktuKmHhUPjr46NU7mgS2Pe+mE5Szbs8Lsc4ycLiWERSgsiInIm8DfgFhG5T0Tui2xZxhhjjIk2qspjny3gnZ9WcM2Jzfnn6a0RsWUrjP9u79WGlMR4Hhk33+9SjN8sJB60UgOiiLwCXAj8HRDgAqBpKBcXkd4iskhEMkTkziD7bxWR+SIyW0S+EpGm3vYjReQnEZnn7bsw4Jy3RWSZiMzybkeG+FyNMcYYc4BUlae+WMTr3y3j8uOa8e8z2lo4NFEjvUYyN/VoyeRFG+jy8Jc0v3M8x//na0bPzPS7NOMHC4kHJZQWxONUdRCwRVUfBLoDpU4TJSLxwEtAH1z31ItFpF2Rw2YCXVW1EzACeMLbvgsYpKrtgd7AcyKSFnDe7ap6pHebFcJzMMYYY8xBeP6rDF6avISLjzmU+/u2s3Book6tqokIsGnnHhTI3JrNXSPnWEiMVRYSD1goATHb+3eXiDQEcoHmIZx3DJChqku9ZTKGAf0DD1DVyaq6y7v7M9DY2/67qi72fl4NrAfSQ3hMY4wxxoTZ/32TwbOTfuf8Lo159OwOFg5NVHp20mKKroaYnZvPkxMX+VKPiQIWEg9IKAFxnNd69yTwK7Ac+DCE8xoBKwPur/K2FecqYELRjSJyDJAELAnY/KjX9fRZEUkOoRZjjIkJo2dmcvx/vrbuVSZs3vhuKU98voj+Rzbkv+d1Ii7OwqGJTqu3Zpdpu4kRFhLLLJRZTB9W1a2q+glu7GEbVQ1lkppg/wcp+sWOO1DkUqArLoQGbm8AvAdcoaoF3ua7gDbA0UBt3LIbwa55rYhMF5HpGzZsCKFcY4yp2EbPzOSukXPI3Jpt3atMWLz703IeGb+AMzrW5+kLjiDewqGJYg3TUsq03cQQC4llEtIspoVUdbeqbgvx8FXsO1axMbC66EEi0hO4G+inqrsDttcExgP3qOrPATWsUWc38BauK2uwWl9T1a6q2jU93XqnGmMqvyc+X0h2bv4+26x7lTlQH077g/vGzKNn23r876LOJMSX6SODMeXu9l6tSUmM32dbSmI8t/dq7VNFJqpYSAxZJP/a/wK0FJHmIpIEXASMDTxARDoDr+LC4fqA7UnAKOBdVf24yDkNvH8FOBuYG8HnYIwxUa2gQPlxyUb+NeI3Vm/LCXqMda8yZTVixir+PWoOJ7dO56VLOpNo4dBUAGd3bsTj53akkddiGB8nPHZOB87uXNIIJxNTLCSGJCFSF1bVPBG5EZgIxANDVHWeiDwETFfVsbgupdWBj70B73+oaj9gAPAX4BARudy75OXejKXvi0g6rgvrLOD6SD0HY4yJVr+v287IXzMZMyuTNdtyqJYUT9WkeHbtyd/v2PQaNlTbhG7sb6v514jfOL5FHV65tAvJCfGln2RMlDi7cyPO7tyI4dNX8q8Rs2lap5rfJZloUxgS3znLhcRBY6BhZ7+riiqiGnRYICJyVEknquqvEakoArp27arTp0/3uwxjjDko67NyGPvbakb+msn8NVnExwl/aVmHc45qzGlt6zFx3lruGjlnv26m1ZLi+ei67nRolOpT5aaimDBnDTd+OJMuTWvxzhXHkJJk4dBUTNtzcjn60Umc36Uxj5zd0e9yTDTassKFxJxtMRMSRWSGqnYt9bgSAuLkEs5TVT31QIsrbxYQjTEV1c7deUyct5ZRMzP5IWMjBQpHNE7lnM6NOOuIhtSpvm/r4OiZmTw5cRGrt2bTMC2Fy49rxts/LicrJ5e3rziaLk1r+/RMTLSbNH8d1w+dQafGqbx7VTeqJ0esk5Ex5eIfw2byzaINTLu7h7WEm+BiLCQedECsTCwgGmMqkrz8Ar7P2MjomZlMnLeO7Nx8GtdK4Ryv61SL9Oplul7m1mwufWMqa7fl8PqgrpzQsk6EKjcV1TeL1nPtuzNo26AG713djZpVEv0uyZiDNuX3DUQ0cW0AACAASURBVAweMo1XLj2K3h0a+F2OiVYxFBLDGhBFpAPQDqhSuE1V3z2oCsuRBURjTLRTVeZmZjFqZiZjf1vNxh27SU1J5MxODTincyO6Nq11UIuTb9i+m8venMrSDTt5cWBnTm9fP4zVm4rsh4yNXPn2L7RIr86H1xxLalULh6ZyyMsv4Lj/fM0RTdJ4fVCpn4lNLIuRkBi2gCgi9wMn4wLiZ0Af4HtVPT8MdZYLC4jGmGi1assuxsxazaiZmWSs30FSfByntqnL2Z0bcUqb9LB2i9q2K5fBb01jTuY2nrqgE+d0bhy2a5uKadqyzQweMo2mh1Tlg2uOpXa1JL9LMiasHvtsAUO+X8a0u3va69uULAZCYjgD4hzgCGCmqh4hIvWAN1S1b3hKjTwLiMaYaLItO5cJc9YwcmYm05ZtBuDoZrU4p3NjzuhYn7SqkfsQs2N3Hte8M52fl23i4f4duPTYphF7LBPdZqzYwqA3p1I/tQofXdd9v/GsxlQGC9dm0fu573iwX3sGH9fM73JMtKvkITHUgBjKCPRsVS0QkTxv8fr1wGEHXaExxsSQPXkFfLNoPaNnZTJpwXr25BVwWJ1q3HZaK87u3IgmtauWSx3VkxN464qjueH9X7ln9Fx27M7j+pNalMtjm+gxe9VWLh8yjfQayXxwzbEWDk2l1aZ+Tdo1qMnIX1dZQDSlsyUwgNAC4nQRSQNeB2YAO4Bp/8/efYdHWWUPHP+e9BAggVCT0EsgEHpHEXAF7AgKFqyriHWbqGxR110VxdW1d8SfFYWIgCIWutRA6BA6pAAJJSEhPbm/P2bChpCQNpN3JnM+z5Mnk3fe986Z4SbMmVuOU6NSSqk6wBjDpiNpfBuXyMKtR0nLyic0yI9bB7RmXJ9wosODa7SusLoCfL159/a+/PnrLUxftJvMnAL+MqqzJbGo2rcjOZ3bP1pPSJAvX9w3iOYNAyq+SCk3Nq5POP/+fhf7UjLo2KyB1eEoV6dJYtV2MRWRtkBDY8xWZwXkDDrFVClVmw6eOMu3cUnMi0viyKksAny9GBXVght6h3NJpyb4entZHSIAhUWGv8/bxpfrE7hrSFueuiYKLy9NEuuy+GMZ3Pz+GgJ9bbUxa2vkWikrpWTkMPiFJdw/rD2Pj+lidTjKXdTB6aYOm2IqIt8Bs4HvjDGHHBCbUkq5rdJ1BqeOjmRs73BOnc1j4VZbEfvNCWmIwNAOTXj08k6M7tacBi5YNsDbS3j+hmjq+/vwwcqDZOYWMH1cND4uksAqx9qXksltH67Fz8eLL+4bpMmh8hjNGgQwrFMTvo1L4rFRkfpBmKocDx5JrMwmNZcBE4GrsU0tnQ0sNMbkOD88x9ARRKWUI8yLS2JazDay8wvPHfP1Fjo3r0/8sUwKigxdWjRgXJ9wrusZTotg95i6Z4zhjSX7eOXnPVwV3YL/TuyNn48miXXJoRNnmfDeGooMfDV5EB2bVa2WplLubsGWZB75Mo4v7h3IkI5aC1ZVQR0aSXTYCKIxZjmwXES8gZHAfcBMoGGNo1RKKTcyY3H8eckhQH6hYdfRDO67tD1je4fTtaX7/WkUER69vBNB/j78a+FOzubG8u6kvgT6Oa7EhrJOwqksbv1gLfmFRXw1ebAmh8ojXRHVnAb+PszZlKgJoqoaDxxJrNRHxCISCIwHpgD9gU+cGZRSSrmi5LTsMo8bA9Ou6uqWyWFJv7+kHS+Oj2bF3lTunLmejJx8q0NSNZScls0tH6zlbF4hn907kMgWukGH8kwBvt5c3aMlP24/xtncAqvDUe6mOEkMCLYliclxVkfkVBUmiCIyG9iFbfTwLaCDMeYRZwemlFKuJiwksErH3dHE/q15/ebebDpymts+XMfps3lWh6Sq6fiZHG79YC3pWfl8+vsBdAsLtjokpSw1rk8EWXmFLN5xzOpQlDvyoCSxMiOIH2NLCqcYY5YYY4qcHZRSSrmiqaMjKb23QaCvN1NHR1oTkJNc2zOM9+/oS/yxDCa+v4aUM26z5FzZpWbkcusHa0nNyGXWPQPoERFidUhKWa5fm0a0ahxIzKYkq0NR7spDksQKE0RjzI/GmMKKzlNKqbquY7P6FBloGOCDAOEhgbwwLpqxvcOtDs3hRnZpzqy7B5B0Opsb311Dwqksq0NSlXTqbB6TPlxHcloOM+/qT982jawOSSmX4OUl3NA7gt/2n+BoetlLBpSqkAckibpNnVJKVdI7y/dT39+HlU+M5OD0q/ntyZF1MjksNrhDKJ/fN4j07HxuencN+1IyrQ5JVSA9K59JH67j0MmzfHhnPwa2D7U6JKVcyrje4RgD8+KSrQ5FubM6niRqgqiUUpVwIDWTH7Yd5fbBbQgOdL2ahs7Sq1UIs+8fREGRYcJ7a9ielG51SKocZ3LyuWPmOvalZPLe7X0Zqjs1KnWBtk2C6NumETGbEqmo1JtSF1WHk8TKbFLza2WOKaVUXfbe8gP4eXtxz9B2VodS67q0aMg3UwYT6OvNLR+sJfbQKatDUqVk5hZw98cb2JF8hrdv68PwyGZWh6SUyxrfJ4K9KZlsTzpjdSjK3ZVOEpe/BK92h2dCbN+3fm11hNVSboIoIgEi0hhoIiKNRKSx/astEFZbASqllNWOpmcTE5fIhH6taNrA3+pwLNGuSRBfTxlMk/r+3P7RelbuTbU6JGWXlVfAPbM2sDkhjTdv7c3voppbHZJSLu3q6Jb4+Xgxd1Oi1aGouqA4SRRvWPocpCcAxvZ9waNumSRebATxfmAj0MX+vfjrO2zlLiokImNEJF5E9onIk2Xc/2cR2SkiW0XkVxFpU+K+O0Vkr/3rzhLH+4rINnubr4uIlG5XKaUc6cOVBykyMHlYe6tDsVR4SCBf3z+YNqH1+P2sWN0q3gXk5Bdy3//FEnvoFK9O7MWY7i2tDkkplxdcz5crujZn/pZk8gt1c37lAI3agE8ZHyDnZ8Ovz9Z+PDVUboJojHnNGNMOeMwY094Y087+1dMY82ZFDYuIN7ZE8kogCrhFRKJKnRYH9DPG9ADmAC/Zr20MPA0MBAYAT4tI8TZs7wCTgU72rzGVf7pKKVU1p8/m8cW6I1zfM4xWjetZHY7lmjbwZ/bkwUSFNeTBzzfxbZx+Am+V3IJC7v90I6v3n2TGjT25rqdO7lGqssb1CefU2TyWx+tsCOUgGeV8aJrufv9PVmaTmmMi0gBARP4uIjEi0qcS1w0A9hljDhhj8oCvgOtLnmCMWWqMKd47fS0QYb89GvjZGHPKGHMa+BkYIyItgYbGmDXGtrL4/4CxlYhFKaWq5ePVh8jOL2TK8A5Wh+Iyguv58tm9AxnQtjF//noLn609bHVIHmNeXBJDpy+h3ZPf0/OZn1i+J5UXbohmfN+Iii9WSp0zrHNTQoP8iNEPuZSjBJfzd7i84y6sMgniP4wxGSJyCbbE7RNso3gVCQcSSvycaD9Wnt8Diyq4Ntx+u8I2RWSyiMSKSGxqqn46pJSquszcAj5ZfYgroprTuXkDq8NxKfX9ffj47v6MjGzG3+dt593l+60Oqc6bF5fEtJhtJKVlY4CcgiJ8vYUAX2+rQ1PK7fh6e3FdrzB+2ZlCela+1eGouuDyp8A38PxjvoG2426mMgliof371cA7xpjvAL9KXFfW2sAy9xMWkUlAP2BGBddWuk1jzPvGmH7GmH5NmzatRLhKKXW+L9cdIT07nwd19LBMAb7evHt7X67tGcb0Rbt5eXG8bhvvRM//sIvs/MLzjuUXGmYsjrcoIqXc2/g+EeQVFrFwm9ZEVA7QYwJc+zoEtwLE9v3a123H3YxPJc5JEpH3gN8BL4qIP5VLLBOBViV+jgAu+A0Ukd8BfwMuM8bklrh2eKlrl9mPR5Q6rr/VSimHyy0o5IOVBxjSIZTerRtVfIGH8vX24r8Te1Hf35s3l+4jM7eAp66JwstL9w9zhKy8Ar7fepTZGxJIycgt85zktOxajkqpuqFbWEM6N69PzKYkbhvYpuILlKpIjwlumRCWVpkEcQK2jWBeNsak2dcBTq3EdRuATiLSDkgCbgZuLXmCiPQG3gPGGGNSSty1GHi+xMY0o4BpxphTIpIhIoOAdcAdwBuViEUppapk7sYkUjJyeWVCL6tDcXneXsLzN0RT39+HD1YeJDO3gOnjovHxrsxniao0YwxbEtOZvSGBBVuSycwtoH2TIBoG+HAmp+CC88NCAstoRSlVERFhXJ8Ipi/azaETZ2nbJMjqkJRyCRUmiMaYLBFJAS4B9gIF9u8VXVcgIg9jS/a8gZnGmB0i8iwQa4yZj21KaX3gG3u1iiPGmOvsieC/sCWZAM8aY4orMz8AzAICsa1ZXIRSSjlQQWER763YT4+IYIZ2DLU6HLcgIvz1qq40CPDllZ/3cDa3gP/e3At/H10fV1mnz+bxbVwSX8cmsPtYBgG+XlwdHcbNA1rRr00jvtuczLSYbedNMw309Wbq6EgLo1bKvY3tFc6LP+4mJi6JP1/R2epwlHIJUtF6ERF5Gtv6wEhjTGcRCQO+McYMrY0AHaFfv34mNjbW6jCUUm5i/pZkHv0yjncn9WVM9xZWh+N2Plp1kH8t3Mmwzk15b1JfAv00SSxPUZFh9f6TfLXhCD/tOE5eYRE9I4KZ0L8V1/YMo2GA73nnz4tLYsbieJLTsgkLCWTq6EjG9r7Y/m9KqYrc/tE6Dp44y4qpI3R6vKrTRGSjMaZfRedVZorpDUBvYBOAMSa5uOyFUkrVNcYY3l66j47N6jMqqrnV4bil31/Sjvr+3jwZs407Z67nw7v6XZDoeLrktGzmbEzk69gEEk9nExzoy60DWzOxfyu6tmxY7nVje4drQqiUg43rE86fZm8h9vBpBrRrbHU4SlmuMglinjHGiIgBEBGdoK2UqrOWxqew+1gGL9/UUz9JroGJ/VtTz8+HP83ezG0frOOTewbQOKgyG2DXXXkFRSzZfZyvNiSwYk8qRQaGdgzl8TFdGBXVXMtVKGWR0d1aUM9vOzGbEjVBVIrKJYhf23cxDRGR+4B7gA+cG5ZSSlnj7aX7CQ8J5PpeYVaH4vau7RlGkL83D3y2iYnvreGzewfSvGGA1WHVun0pmXwdm8DcjYmcPJtHi4YBPDSiIzf1bUXr0HpWh6eUx6vn58OV3Vvy/dajPHNdN/2wRnm8ymxS87KIXAGcASKBp4wxPzs9MqWUqmXrD54i9vBp/nldN3x1B06HGNmlObPuHsC9n2zgpnfX8Pm9A2nVuO4nRVl5BSzcepSvNyQQe/g0Pl7C5V2bcXP/1gzr3BRvHZ1WyqWM7xPO3E2J/LTzONf11A8IlWerzAgi9oTwZxFpApx0bkhKKWWNt5buIzTIjwn9WlV8sqq0wR1C+fy+Qdw5cz1Xv76SAF9vUjNy69wmK/8rT3GEBVuO2spTNA1i2pVdGNcngqYN/K0OUSlVjkHtQwkLDiBmU6ImiMrjlZsg2msNTgdOAf8CPgWaAF4icocx5sfaCVEppZxve1I6y/ekMnV0pO666QS9WoVw/2XteenH+HO1/JLSspkWsw3ArZPE4vIUszckEH88g0Bfb67u0ZKJ/W3lKexlnJRSLszLSxjbO5x3l+8nJSOHZg08bzq8UsUuNoL4JvBXIBhYAlxpjFkrIl2ALwFNEJVSdcY7y/fTwN+HSYPaWB1KnfX52iMXHMvOL+SJuVtZvf8ELYIDaRkcYP8KpGVIAA38fVwywSoqMvy2/wSzNyScV57i+RuiubZnSxrorq1KuZ1xfcJ5e9l+5m9O5t5L21sdjlKWuViC6GOM+QlARJ41xqwFMMbsdsX/rJVSqroOpGbyw7ajTLmsA8GB+sbeWZLTsss8nltQxPI9qaRk5FK6NG+QnzctQ2yJY4uGAeduW5VEJqdl801sIt9srFp5CqWU6+vYrAE9I4KZuylJE0Tl0S6WIBaVuF36f/VS/4UrpZT7em/5Afy8vbhnaDurQ6nTwkICSSojSQwPCeS3J0eSX1hESkYux9KzSU7L4Vh6Dsnp2RxLz+Foeg57jpefRLYIDiAsJLDMJLJFcAANAyqXRJZViP6q6Jb8ustenmJvKkbLUyhVZ43vG8FT3+1gZ/IZosL0Qx/lmS6WIPYUkTOAAIH229h/1onZSqk64Wh6NjFxidzcv7VuIuJkU0dHMi1mG9n5heeOBfp6M3V0JAC+3l6EhwQSHhJI33Jm+jo6iWwRHECYPYlcsus4f/12+7n4ktKyeeybLfzt222czSukRcMAHtbyFErVadf0CONfC3fybVwiUWFRVoejlCXKTRCNMfqRqFKqzvtw5UGKDEweptOJnK14I5rSI3RV2aDGmUmkcOH0mIIig48xfHxXfy1PoZQHaBzkx4jIZszbnMwTY7rgoyWPlAeqVJkLpZSqi06dzeOLdUe4vmeYR9TmcwVje4c7fcfS6iaRz/2wq8xzc/OLGNGlmRMjVkq5knF9Ivhp53FW7TvB8Ej93VeeRxNEpZTHmrX6ENn5hUwZ3sHqUFQtKyuJnLX6UJlrJMNCAms5OqWUlUZ0aUpIPV9iNiVpgqg8ko6bK6U8UmZuAZ+sPsSoqOZ0bt7A6nCUC5g6OpLAUhvOlFwjqZTyDP4+3lzbI4zFO46RkZNvdThK1TpNEJVSHunLdUdIz87nwREdrQ5FuYixvcN5YVw04SGBCLbdVV8YF+30KbFKKdczrk84uQVFLNp2zOpQlKp1OsVUKeVxcgsK+WDlAYZ0CKVXqxCrw1EupDbWSCqlXF+vViG0bxLE3E2JTOjfyupwlKpVOoKolPI4czcmkZKRy4PDdfRQKaXUhUSEcX3CWXfwFAmnsqwOR6lapQmiUsqjFBQW8d6K/fSMCGZox1Crw1FKKeWiimcTzItLsjgSpWqXTjFVysPMi0uqUR06d/f9tqMcPpnFtEl9EdGadkoppcoW0ageg9o3JiYuiYdHdtT/M1SF6sp7LKeOIIrIGBGJF5F9IvJkGfcPE5FNIlIgIjeWOD5CRDaX+MoRkbH2+2aJyMES9/Vy5nNQqi6ZF5fEtJhtJKVlY4CktGymxWzzmE9HjTG8s2w/HZvVZ1RUc6vDUUop5eLG9Yng4ImzxCWkWR2KcnF16T2W0xJEEfEG3gKuBKKAW0QkqtRpR4C7gC9KHjTGLDXG9DLG9AJGAlnATyVOmVp8vzFms7Oeg1J1zYzF8WTnF553LDu/kBmL4y2KqHYtjU9h97EMplzWAS8v/SRYKaXUxV3ZvQUBvl7EbEq0OhTl4urSeyxnjiAOAPYZYw4YY/KAr4DrS55gjDlkjNkKFF2knRuBRcYYXSGsVA0ll1EE/GLH6xJjDG8t3U94SCDX9wqzOhyllFJuoEGAL6O7tWDBlqPkFhRWfIHySOnZ+STVofdYzkwQw4GEEj8n2o9V1c3Al6WOPSciW0XkVRHxL+siEZksIrEiEpuamlqNh1WqbikoLCLQz7vM+8JCAms5mtq3/uApNh4+zeRh7fH11v25lFJKVc64PhGkZ+ezZFeK1aEoF5OdV8g7y/Yz7KWl5Z7jju+xnPkuqaz5W6ZKDYi0BKKBxSUOTwO6AP2BxsATZV1rjHnfGNPPGNOvadOmVXlYp5sXl8TQ6Uto9+T3DJ2+xC3nJiv3kpVXwP2fbiQrrxCfMqZW3jG4jQVR1a63l+0nNMiPCf20npVSSqnKG9ohlGYN/Jm7Sd+vKZv8wiI+W3uYy2Ys5cUfd9OndQiPje5MoO/5H8QH+nozdXSkRVFWnzN3MU0ESr4TiwCSq9jGBOBbY0x+8QFjzFH7zVwR+Rh4rEZR1rLiBazFc5SLF7ACbrnLkXJ9JzNz+f0nsWxNTONfY7vTwN/n3A5bzRr6k5lTwOfrjnBj3whC65c5IO/2tiels3xPKlNHR5Y7iqqUUkqVxcfbi7G9w5m56iAnM3Pr7P+VqmJFRYYFW5N55ec9HD6ZRf+2jXjrtj70b9sYgIiQenViF1NnJogbgE4i0g5IwjZV9NYqtnELthHDc0SkpTHmqNj2Gh4LbHdEsLWlvAWsL/642y07kBXqyhbCteHwybPcOXM9R9NzeGdSX0Z3awGc/2HEpiOnueX9tUz+dCOf3zuQAN+6l0C9s3w/Dfx9mDSo7o+UKqWUcrxxfcJ5f8UBFmxJ5q6h7awOR9UyYwxLdqcwY3E8u49l0LVlQz6+qz/DI5ueV/5kbO/wOvGe1GlTTI0xBcDD2KaH7gK+NsbsEJFnReQ6ABHpLyKJwE3AeyKyo/h6EWmLbQRyeammPxeRbcA2oAnwb2c9B2cob6Hq0fQcHvhsIwu3JpOVV1DLUbmPurSFsLNtTUxj/DurScvO54v7Bp5LDkvr07oRr0zoxcbDp5k6ZytFRVWaCe7yDqRm8sO2o0wa3IbgQF+rw1FKKeWGurRoSFTLhsTo+w2Ps+7ASW56dw2//ySWnPxCXr+lN98/cgkjujSrs7UxnTmCiDHmB+CHUseeKnF7A7app2Vde4gyNrUxxox0bJS1KywksMxdjoL8vNlw6DSLth8jwNeLEZHNuLpHS0Z2aUY9P6f+M7mNrLwC/rVwZ7lbCNeFT2wcZWl8Cg99vonGQX58cs8AOjStf9Hzr+7RksOnInnpx3jahtbjL6Pcb758ed5bfgA/by/u0U98lVJK1cD4vhH8a+FO9h7PoFPzBlaHo5xse1I6MxbHs3xPKs0b+vP8DdHc1C/CIza608yjlk0dHXneGkSwLWB97oZoru0ZxoZDp/h+61EWbT/m8cmiMYZdRzNYsTeVFXtSiT10mrzCsiuiuOMWws7ydWwC02K20aVFAz6+uz/NGgRU6roHLuvA4RNZvLFkH21Cg7ixb5mf3biVo+nZxMQlcsuA1jRtoGtGlFJKVd91PcN4/oddxMQl8cSYLlaHo5zkQGom//l5D99vPUpIPV+mXdmFO4e0rZNLcMrjOdmGiyge5SpvDd2g9qEMah/KM9d188hk8dTZPFbuTWXFnhOs3JtKSkYuAF1aNOCuoW2ZuzGRk2fzLriunr83OfmFHvXLW5oxhjeW7OOVn/dwaacmvDOpL/X9K99HRIR/39CdhNNZTIvZSnhIIIM7hDoxYuf7YMVBigzcd2l7q0NRSinl5po28Oeyzk2ZF5fEY6Mi8S5jV3Dlvo6mZ/PaL3v5ZmMi/j5ePDKyI/cNa0/DAM9bniLG1K31RmXp16+fiY2NtTqMaissMmw4dIofth3lh23HOJGZW2eSxfzCIuKOpLFiTyor9qayLSkdY6BRPV8u6dSUYZ2aMKxzU5o3tI2Cld4FFsDbSygsMnRp0YA3buntkdM+CgqL+Md32/lyfQLj+oQzfVwP/HyqNwUiPTuf8e+sJuVMDjEPDqVjs4tPT3VVp87mMXT6Eq7s3oJXJvayOhyllFJ1wMKtyTz8RRyf3zuQoR2bWB2OcoBTZ/N4Z9k+PllzGAzcOrA1D43oWCdnHonIRmNMvwrP0wTRvdSFZDHhVBYr9qayPD6VNftPkpFbgLeX0LtVCMM6N2VY56ZEhweX+8lcWbuYBtfz5bGvt5CZW8A/ronitoGt6+zC4dKy8gp45Is4ft2dwkMjOvDYqMgaP/eEU1mMfes3gvx9+PbBIW65pfcrP+/h9V/38tOfhtHZAz80UEop5Xg5+YX0f+4XrohqzisT9MNHd5aZW8BHKw/ywcoDZOUVMK5PBH/8XSciGtWzOjSn0QSxhLqUIJbkLsliVl4Baw+cZMWeE6zYk8qBE2cBCA8JZFjnplzWuQmDOzSp8Q6TKRk5/OXrLazce4JRUc15cXwPGgX5OeIpuKyTmbnc80ks2xLT+Of13bndgWUcistfdA8PdrvyF5m5BQydvoSB7Rrz/h0V/h1USimlKm1azFa+25zMhr/9jqAqLOVQriEnv5DP1x3h7aX7OHk2j9HdmvPYqEiPmIGmCWIJdTVBLMmVksXyNpcJ8PVicPvQc6OE7ZsEOXyUr6jIMPO3g7z4425Cg/x5dWIvt19HV56SNQ7fuKU3o8opY1ET3289ykNfbOLanmG8NrEXXm6y3uL9Fft5/ofdzHtoKL1ahVgdjlJKqTpkw6FT3PTuGl6Z0JNxfdx/QzdPUVBYRMymJP77yx6S03MY2jGUqaO7eNT7BE0QS/CEBLEkK5LFkpvLrNibSmqJzWWGdW7KsE5N6de2Ua2NQm1PSufRL+M4ePIsDw3vyB9+16lObUu8JSGNe2ZtoMgYPryzP33bNHLaY729bB8v/RjPIyM7ukX5i9yCQi59cSkdm9Xni/sGWR2OUkqpOsYYw7AZS2nTOIjP7h1odTiqAsYYftx+jJd/imd/6ll6RgTz+JguHrmGtLIJoo6L10HeXnJuN9Snr+12Lll05G6oVd1cprZ1Dw9mwSOX8M8FO3hz6T5+23+C12/uTavG7j+vfOnuFB78fBOh9StX47Cm3K38xdyNSaRk5OraEKWUUk4hIozrHcHrS/ZyND2blsGBVoekymCMYdW+E8xYHM/WxHQ6NqvPu5P6Mrpbc4/Zp6K6dATRg5QcWVy0/RipGWWPLJa1CczY3uEknMpi+R7btNHSm8tcZp822v0im8tYZeHWZKbFbAMD/76hO9f3Crc6pGr7ekMC076teo3DmsovLOKuj9ez/uAp/u+egS47bbegsIiR/1lOo3q+zHtoqP4HoJRSyikOnzzLZTOW8cSYLjwwvIPV4ahSNh05zYwf41lz4CThIYH86YrO3NA73OXeo9Y2nWJagiaIFyovWezcvD67jmaQX/i/fuHtJTSu50tqpq3+oKM3l6kNiaez+MNXm9l4+DTj+oTz7PXdq1Qj0GrGGF7/dR+v/lK9GoeOUFz+IjUjl5gHhzh95LI6vtucxB++2sy7k/oyprvj12QqpZRSxW58ZzXp2fn89Kdh+oGki4g/lsHLP8Xz887jhAb58fDIjtw6sDX+Pu6zK2rNWgAAIABJREFU0Z4zaYJYgiaIF1cyWfxs7WGKyugS/j5ePHllF6dtLlMbCgqLeH3JPt5cspfWjevx2s296ekGC5NL1zh8cXwPy9ZTunL5C2MMV762koIiw09/HOY2G+oopZRyT1+sO8Jfv93GgocvIToi2OpwPFrCqSxe/XkP325Oor6fD5OHteeeS9rpLrOlaIJYgiaIldfuye8pq0cIcHD61bUdjlOsP3iKP34VR0pGLo+NjmType1dNpkoWePw4REd+cuozpYn565a/mLJ7uPcMyuW/9zUk/Euvk5SKaWU+0vPyqf/879w64DWPHNdN6vD8Qill0Hdf1k79qWc5cv1R/AS4a4hbZlyWYc6X+asuiqbINadbR2VQ4SFlL3Qurzj7mhAu8Ys+sMwRnVrzvRFu7lj5npSzuRYHdYFTmbmcssH61gan8K/x3bnsdGRlieHAH1aN+KVCb3YePg0U+dspaisIedaZozhraX7CQ8J5LpeYVaHo5RSygME1/Pliq7Nmb8lmfzCIqvDqfPmxSUxLWYbSWnZGCApLZunvtvJp2sOc1O/ViyfOoJpV3XV5NABNEFU55k6OpLAUiNCgb7eTB3t+uUNqiK4ni9v3dqH6eOi2Xj4NGNeW8mvu45bHdY5h0+eZfw7q9l99AzvTurLpEFtrA7pPFf3aMnjYyJZsCWZV3/ZY3U4rD94io2HTzN5WPs6Vc5EKaWUaxvXJ5xTZ/NYHp9qdSh13ozF8WTnF15wvFlDf56/IZoWwdbsnF8X6TspdZ6xvcN5YVw04SGBCLYNaV4YF83Y3u6782d5RISbB7RmwSOX0KJhAL//JJZn5u8gp4w/PrVpS0Ia4962LXz/4r5BjOrmmputPHBZByb2a8UbS/YxZ2OipbG8vWw/oUF+TOjXytI4lFJKeZZhnZsSGuTH3E3W/j/oCZLSsss8nnImt5Yjqft05aa6wNje4XUyISxPx2b1+fahIby4KJ6Zvx1k7YGTvHFLbzo1b1DrsdR2jcOaEBH+fUN3EtOymBazlfCQQEvKX2xPSmf5nlTb6Lefa6yHVEop5Rl8vb24rlcYn689QlpWHiH1dHqjo6Vk5PD897vKvb8uLYNyFTqCqBTg7+PNU9dG8fHd/UnNyOWaN1bx2drD1OYmTl9vSODe/4ulfdMgly0jUZqvtxdv39aXNqFBTPlsI/tTM2s9hneW7aeBvw+3D3atabhKKaU8w/g+EeQVFrFw61GrQ6lTCosMn6w+xOUvL+eHbccYHdWcAN/zU5e6uAzKFWiCqFQJIyKbseiPlzKgXWP+Pm8793+6kdNn85z6mMYYXvtlL4/P3cqQDqHMvn8wzRq4zzz64EBfPr6rPz5ewt0fb+BkZu1N9TiQmskP248yaXAbGga4fj1OpZRSdU+3sIZ0bl6fGJ1m6jBbEtK4/q1VPD1/Bz1bhfDjHy/lvTv6MX1cD49YBmU1LXOhVBmKigwzfzvIiz/uJjTIn1cn9nLK9ElXqnFYU1aUv3hizlbmbU5i1RMjadrAdWoyKqWU8izvLd/PC4t2s/Sx4bRrEmR1OG4rPSufGT/t5vN1R2ha35+nro3i6uiWLrGLe13gEmUuRGSMiMSLyD4RebKM+4eJyCYRKRCRG0vdVygim+1f80scbyci60Rkr4jMFhGd7K0czstLuPfS9nz74FDq+Xlz64dreXlxvEO3sc7KK+D+Tzfy5foEHh7Rkf/c1NNtk0Oo/fIXR9OziYlLZGL/VpocKqWUstTY3uF4CXyro4jVYoxh7sZERv5nGV+sO8LdQ9rx618u45oeYZocWsBp70ZFxBt4C7gSiAJuEZGoUqcdAe4CviijiWxjTC/713Uljr8IvGqM6QScBn7v8OCVsuseHsyCRy7hpr4RvLl0HxPeW0PCqawat+uqNQ5rqjbLX3yw4iBFBu67tL1TH0cppZSqSPOGAQzt2ISYuCSXqA/sTvYcz2Di+2v5yzdbaB1ajwWPXMJT10bRQJeOWMaZwxUDgH3GmAPGmDzgK+D6kicYYw4ZY7YClRqWEds76JHAHPuhT4CxjgtZqQsF+fvw0o09efPW3uxLyeSq11by3eakarfn6jUOa6o2yl+cOpvHl+uPcH3PMFo1rueUx1BKKaWqYnyfCBJPZ7Ph0CmrQ3ELWXkFTF+0m6teW0n8sQxeGBfN3ClD6BYWbHVoHs+ZZS7CgYQSPycCA6twfYCIxAIFwHRjzDwgFEgzxhSUaLPMlakiMhmYDNC6desqhq7Uha7pEUavViH84avN/OGrzSzfk8qz13envn/lf422JKRxz6wNFBnDF/cNom+bRk6M2Bq1Uf5i1upDZOcX8sDwDg5tVymllKquUd2aE+TnTcymJAa2r/2yT+7CGMNPO4/z7IKdJKVlM6FfBE+M6UJofV0u4iqcOYJY1ny5qoy5t7YvorwV+K+IdKhKm8aY940x/Ywx/Zo2bVqFh1WqfBGN6jF78iAevbwT8+KSuOb1lWxJSKvUtUt3p3Dz+2sJ9PNmzgND6mRyWMyZ5S8ycwuY9dtBRkU1t6RWpVJKKVWWen4+XBndku+3HSUnv9DqcFxSwqks7v0klvs/3Uh9fx++mTKYl27sqcmhi3FmgpgItCrxcwSQXNmLjTHJ9u8HgGVAb+AEECIixUM2VWpTKUfw8fbiz1d05qvJg8krKGL8O6t5d/n+i645cMcahzXlrPIXX6w7zJmcAh4c0dEh7SmllFKOMq5POJm5Bfy087jVobiU3IJC3lq6jyteXc6aAyf521VdWfjoJfRv29jq0FQZnJkgbgA62Xcd9QNuBuZXcA0AItJIRPztt5sAQ4GdxlaTYylQvOPpncB3Do9cqUoY0K4xi/4wjFHdmjN90W7umLmelDM5553j7jUOa6pV43p8cGc/jp/JYfKnG2v8iWpuQSEfrjzIkA6h9GoV4qAolVJKKccY1C6UsOAArYlYwup9J7jytZXMWBzPyC7N+PUvl3HfsPZuvXN7Xee0NYjGmAIReRhYDHgDM40xO0TkWSDWGDNfRPoD3wKNgGtF5J/GmG5AV+A9ESnClsRON8bstDf9BPCViPwbiAM+ctZzUKoiwfV8eevWPszekMA/F+xkzGsrGdcnnEXbjpGclk2gnzdZeYVuX+OwJorLXzz0xSamztnKaxN74eVVvR1b525MIiUjl1cn9nJwlEoppVTNeXkJN/QJ551l+0nJyPGoD4VLS8nI4bnvd/Hd5mRaN67Hx3f3Z0RkM6vDUpXgzE1qMMb8APxQ6thTJW5vwDZNtPR1q4Hocto8gG2HVKVcgohw84DW9GvbmDs+WseHKw+euy8rrxAfL+HSjk08MjksdnWPlhw+FclLP8bTNrQefxkVWeU2CgqLeHf5fnpGBDPEwZveKKWUUo5yQ+8I3lq6n/mbk7nXA0sxFRYZPlt7mJcXx5NbUMSjl3fiweEdCPD1tjo0VUme+45VKQfr2KzsdYUFRYaXf3JuTUB3UNPyF99vO8qRU1k8MLxjnagZqZRSqm7q2Kw+PVuFMHdT9UtiuavNCWlc/9Yqnp6/g16tQ1j8p2H8+YrOmhy6GaeOICrlaY6m55R5PDktu5YjcT01KX9hjOGdZfvp2Kw+o6KaOzlSpZRSqmbG9wnnqe92sDP5DFFhDa0Ox+nSs/J5afFuvlh/hKb1/Xnz1t5cHd1SP9B1UzqCqJQDhYUEVum4p6lu+Yslu1PYfSyDBy7rUO31i0oppVRtuaZHGL7ewrdxdXuzGmMMczcmMvI/y/hy/RHuHtKOX/9yGdf0CNPk0I1pgqiUA00dHUlgqWkUgb7eTB1d9TV3dVVVy18YY3h72X7CQwK5rldYLUWplFJKVV/jID9GRDZj3uZkCgqLrA7HKfYcz2Di+2v5yzdbaBNajwWPXMJT10bRIMDX6tBUDWmCqJQDje0dzgvjogkPCUSA8JBAXhgXzdje4VaH5lKqUv5i/cFTbDx8msm6JbZSSik3Mq5PBKkZuazad8LqUBwqK6+A6Yt2c9VrK9lzPIPp46KZM2UI3cKCrQ5NOYiuQVTKwcb2DteEsBL6tG7EqxN78eDnFy9/8fay/TSp78fE/q0siFIppZSqnhFdmhJSz5eYTUkMrwPlHYwx/LTzOM8u2ElSWjYT+kXw5JVdaRzkZ3VoysE0QVRKWeaq6JY8Pqb88hfbk9JZvieVqaMjdQc0pZRSTpWfn09iYiI5OWVvOFcd71zdnLN5hezYuRMvN16TV1BURHpWPl75RTw3ohEh9Zrj7+PF8SP7OW51cOoCAQEBRERE4Otbvem+miAqpSz1wGUdOHwiizeW7KNNaBA39v1fadR3lu2ngb8Ptw9uY2GESimlPEFiYiINGjSgbdu2DttgJSu3gH2pmbRsFEjjIH+HtFmbiozhREYuKRm5hIRAZMMAQuv7uXWyW9cZYzh58iSJiYm0a9euWm1ogqiUslR55S8OpGbyw/ajTLmsAw11wbtSSikny8nJcWhyCBDo542/jzens/LdIkE8nZXH8fQc8gqL8PHyAoGCwiKCA31pGRyIn4/uBeDqRITQ0FBSU1Or3YYmiEopyxWXvxj/zmrumbWeBgG+pGTYdjdtGRxgcXRKKaU8haNLM4gIjer5cuxMDnkFhfj5uO5yidNZeSSdzqbIGMA2rRSgaQN/WgZruS53UtN+rB8DKKVcQnCgL7cMaEV2ftG55BDghR92My8uycLIlFJKqeoLqWfbxOV0Vr7FkVyoqMiQlVvAyczc85LDktJdMG7lXJogKqVcxsxVhy44lp1fyIzF8bUfjFJKKeUAfj5e1Pf3IS0rD1NGAlbs0KFDdO/e3SkxLFu2jKuvvprM3AI+nT2XJ596lj3HM9iRfIZ9qZkkpZWdHALk1bE6jlV9nWfNmkVycnKF5zz88MM1Dc1l6BRTpZTLSE7LrtJxpZRSyirz4pKYsTie5LRswkICmTo6stwyVyH1/Eg8nUVWXiFB/s5/+11QWER2fqHtK6+QI6fOkpFbwIHUTHoOvZx+w67Az9uLhgG+BPp5E+jrzYHUzDKTQT+raxBv/Rp+fRbSEyE4Ai5/CnpMqLWHnzVrFt27dycsLKzWHrNYQUEBPj61n67pCKJSymWEhZS9xqG840oppZQV5sUlMS1mG0lp2RggKS2baTHbyl0SERzoi5cIaVl5F223oKCAO++8kx49enDjjTeSlZXFs88+S//+/enevTuTJ08+Nwr5+uuvExUVRXR0D8bfNIHjZ3LYeTiFcTffTq8+/ejfry9ffh1Ddn4hft5e+Pt407ZJEBt+iuGt5/5K2yZBPPmHKfzjib8wfNgljBnSk1++n38ullnvvs6t14xk/BVDefrppx322lXJ1q9hwaOQngAY2/cFj9qO10BlX+c5c+YQGxvLbbfdRq9evcjOzmbDhg0MGTKEnj17MmDAADIyMgBITk5mzJgxdOrUiccff/zcY9WvX5+//e1v9OzZk0GDBnH8uK0wyOHDh7n88svp0aMHl19+OUeOHAHgrrvu4s9//jMjRozgiSee4JlnnuHOO+9k1KhRtG3blpiYGB5//HGio6MZM2YM+fmOnwKsI4hKKZcxdXQk02K2kZ1feO5YoK83U0dHXuQqpZRSyrH+uWAHO5PPlHt/3JG0C0bbsvMLeXzOVr5cf6TMa1o1rsfdQ9vSMsSUWyYiPj6ejz76iKFDh3LPPffw9ttv8/DDD/PUU09hjOG2Sbcze+63DL/iSp57/gUWr9mKl68vZ9LTOX4mh7f+8xLDhg/nvQ8+JPfsGUYOG8q9N1/PseBA/H1sI4beXuePDx09epRVq1axe/durrn2Wq66bizLlvxC4qEDrPhtDSGBvlx33XWsWLGCYcOGVfGVrMCiJ+HYtvLvT9wAhbnnH8vPhu8eho2flH1Ni2i4cvpFH/ZirzPA7bffzsKFC7nxxht58803efnll+nXrx95eXlMnDiR2bNn079/f86cOUNgoO1D7M2bNxMXF4e/vz+RkZE88sgjtGrVirNnzzJo0CCee+45Hn/8cT744AP+/ve/8/DDD3PHHXdw5513MnPmTB599FHmzZsHwJ49e/jll1/w9vbmmWeeYf/+/SxdupSdO3cyePBg5s6dy0svvcQNN9zA999/z9ixYyv3eleSjiAqpVzG2N7hvDAumvCQQAQIDwnkhXHR5U7ZUUoppaxQ3rq8i63XC/DxorDIkJFd/ohPq1atGDp0KMYYJtx8C0uXr2DuwsX06tufyKju/PLrEtZs3EJqRh6do7rx1z9M5rcfv6VTi2C6hQWzcfUyPnzzVa64dCDXjLmCnJyccyNT5Rk7dixeXl5ERUWRmpJCl5YN2Re3mvWrljFy6ED69OnD7t272bt3b6VeG4cqnRxWdLySil9ngEmTJrFq1SqWLl3KwIEDiY6OZsmSJezYseOC6+Lj42nZsiX9+/cHoGHDhuemgF5++eUEBwcTEBBAVFQUhw8fBsDPz49rrrkGgL59+3Lo0CEA1qxZw6233grYEtJVq1ade5ybbroJb+//7Xh75ZVX4uvrS3R0NIWFhYwZMwaA6Ojoc+05ko4gKqVcytje4ZoQKqWUstTT13a76P1Dpy8hqYz18eEhgcy+f3CZ1xhj2Jl8hoTT2Rw+lYWftxfNgwMIDvQlN7+QtKw8igzsS8kkJ7+QhNPZZOcX8eRf/sC8n1bQvm1rXpvxPL7ehm7hDVn282JWrFjB/PnzGTZjOjt27MAYw9y5c4mMPH/mTfG0xrL4+/+vPmPx9FVjDNOmTeP++++/6OtQYxWM9PFqd/v00lKCW8Hd31f7YUuXgRARHnzwQWJjY2nVqhXPPPMMOTk5F1xnjCm3hETJ19Hb25uCggIAfH19z11T8vjFYgoKCiqzbS8vr/Pa8/LyKre9mtARRKWUUkoppapg6uhIAn3Pr2lY0ZKItOx8igzndgvNKywi4VQWO5LS2ZuSydH0HJISE4iLXUfjID9W/vgdo0Zeho+X0K9LG4J9i/hh/jx8vb3AGBISEhgxYgQvvfQSaWlpZGZmMnr0aN54441ziV5cXFy1nt/o0aOZOXMmmZmZACQlJZGSklKttmrk8qfAt9Q+BL6BtuM1cOTIEdasWQPAl19+ySWXXAJAkyZNyMzMZM6cOefObdCgwbl1hl26dCE5OZkNGzYAkJGRUe0EbciQIXz11VcAfP755+dicAVOHUEUkTHAa4A38KExZnqp+4cB/wV6ADcbY+bYj/cC3gEaAoXAc8aY2fb7ZgGXAen2Zu4yxmx25vNQSimllFKqWPFMl8ruYgpwPD0Hw4WlJLxECG8UiH92EF27duXX+d/wryf/RKdOnXj04YfIPJNOdHQ0bdu2PTe1sbCwkEmTJpGeno4xhj/96U+EhITwj3/8gz/+8Y/06NEDYwxt27Zl4cKFVX5+o0aNYteuXQwebBsNrV+/Pp999hnNmjWrcls1UrxbqYN3Me3atSuffPIJ999/P506deKBBx7g9OnTF7zOYNs0ZsqUKQQGBrJmzRpmz57NI488QnZ2NoGBgfzyyy/ViuH111/nnnvuYcaMGTRt2pSPP/64Rs/JkeRi9Vhq1LCIN7AHuAJIBDYAtxhjdpY4py22JPAxYH6JBLEzYIwxe0UkDNgIdDXGpNkTxIXF51ZGv379TGxsrEOel1JKKaWUqnt27dpF165dndb+1sS0cu/rERHitMdVnqms/iwiG40x/Sq61pkjiAOAfcaYA/aAvgKuB84liMaYQ/b7zlvRa4zZU+J2soikAE2B8n+zlFJKKaWUclF+3l6uWWdQqVKc2SPDgZKrShPtx6pERAYAfsD+EoefE5GtIvKqiPiXc6lSSimllFIuoXlwwAXlLbxEaB4cYFFESpXNmQliWVv8VGk+q4i0BD4F7jbGFH/kMg3oAvQHGgNPlHPtZBGJFZHY1NTUqjysUkoppZTyQM5aegXQqJ4f4Y0Cz40Y+nl7Ed4okEb1/Jz2mMoz1bQfO3OKaSLQqsTPEUByZS8WkYbA98DfjTFri48bY47ab+aKyMfY1i9ewBjzPvA+2NYgVi10pZRSSinlSQICAjh58iShoaHlljKoqUb1/DQhVE5ljOHkyZMEBFR/ZNqZCeIGoJOItAOSgJuBWytzoYj4Ad8C/2eM+abUfS2NMUfF9ps7Ftju2LCVUkoppZSniYiIIDExEZ15ptxdQEAAERER1b7eaQmiMaZARB4GFmMrczHTGLNDRJ4FYo0x80WkP7ZEsBFwrYj80xjTDZgADANCReQue5PF5Sw+F5Gm2KawbgamOOs5KKWUUkopz+Dr60u7du2sDkMpyzmtzIUr0TIXSimllFJKKU9W2TIXuq+uUkoppZRSSilAE0SllFJKKaWUUnYeMcVURFKBwxc5JRhIr2RzlT23Muc1AU5U8nHdVVVeW2dyZhyObLsmbVXnWkf3fe33Np7Q7x3Zvrv3+8qc5wn9Hlyj72u/r/k1+l6narTf115bVb1W+/352hhjmlZ4ljHG47+A9x19bmXOw7ZZj+XP31VeW3eNw5Ft16St6lzr6L6v/d7xfcKV43BU++7e7ytznif0e0f2CVeOQft91c7zhL6v/b722qrqtdrvq/elU0xtFjjh3Kq0WZe5yuvgzDgc2XZN2qrOtY7u+67y7201V3kdnB2Ho9p3935f3TjqIld4HbTf1/wa7fdV4wqvg7v0+5q2VdVrtd9Xg0dMMXVVIhJrKrGTkFJ1ifZ75Ym03ytPpX1feSJ37/c6gmit960OQCkLaL9Xnkj7vfJU2veVJ3Lrfq8jiEoppZRSSimlAB1BVEoppZRSSillpwmiUkoppZRSSilAE0SllFJKKaWUUnaaICqllFJKKaWUAjRBdGkiEiQiG0XkGqtjUao2iEhXEXlXROaIyANWx6NUbRCRsSLygYh8JyKjrI5HqdogIu1F5CMRmWN1LEo5k/39/Cf2v/O3WR1PZWiC6AQiMlNEUkRke6njY0QkXkT2iciTlWjqCeBr50SplGM5ot8bY3YZY6YAEwC3rR+kPIeD+v08Y8x9wF3ARCeGq5RDOKjfHzDG/N65kSrlHFX8HRgHzLH/nb+u1oOtBk0QnWMWMKbkARHxBt4CrgSigFtEJEpEokVkYamvZiLyO2AncLy2g1eqmmZRw35vv+Y6YBXwa+2Gr1S1zMIB/d7u7/brlHJ1s3Bcv1fKHc2ikr8DQASQYD+tsBZjrDYfqwOoi4wxK0SkbanDA4B9xpgDACLyFXC9MeYF4IIppCIyAgjC1sGyReQHY0yRUwNXqgYc0e/t7cwH5ovI98AXzotYqZpz0N97AaYDi4wxm5wbsVI156i/90q5q6r8DgCJ2JLEzbjJ4JwmiLUnnP99egC2zjKwvJONMX8DEJG7gBOaHCo3VaV+LyLDsU3F8Ad+cGpkSjlPlfo98AjwOyBYRDoaY951ZnBKOUlV/96HAs8BvUVkmj2RVMqdlfc78DrwpohcDSywIrCq0gSx9kgZx0xFFxljZjk+FKVqTZX6vTFmGbDMWcEoVUuq2u9fx/YGQil3VtV+fxKY4rxwlKp1Zf4OGGPOAnfXdjA14RbDnHVEItCqxM8RQLJFsShVW7TfK0+k/V55Iu33ytPVmd8BTRBrzwagk4i0ExE/4GZgvsUxKeVs2u+VJ9J+rzyR9nvl6erM74AmiE4gIl8Ca4BIEUkUkd8bYwqAh4HFwC7ga2PMDivjVMqRtN8rT6T9Xnki7ffK09X13wExpsJlcEoppZRSSimlPICOICqllFJKKaWUAjRBVEoppZRSSillpwmiUkoppZRSSilAE0SllFJKKaWUUnaaICqllFJKKaWUAjRBVEoppZRSSillpwmiUkoppxORV0XkjyV+XiwiH5b4+T8i8ucK2lhdicc5JCJNyjg+XESGlHPNdSLyZAXthonIHPvtXiJyVRWvv0tE3rTfniIid1T0XCp6DtVtxxnssS20Og6llFI152N1AEoppTzCauAm4L8i4gU0ARqWuH8I8MeyLixmjCkzwauk4UCmPY7S7c4H5lfw2MnAjfYfewH9gB8qe32ptt6t7LmlDKfEc6hBO0oppVS5dARRKaVUbfgNWxII0A3YDmSISCMR8Qe6AnEAIjJVRDaIyFYR+WdxAyKSaf/uJSJvi8gOEVkoIj+IyI0lHusREdkkIttEpIuItAWmAH8Skc0icmnJwEqN7s0SkddFZLWIHChuV0Taish2EfEDngUm2tuaWOr6a0VknYjEicgvItK89AshIs+IyGP2UcnNJb4KRaRNWW2U9RyK27G32UtE1tpfs29FpJH9+DIReVFE1ovIntLP3X5OSxFZYW93e/E5IjLG/jpuEZFf7ccG2F+bOPv3yDLaCxKRmfZ/wzgRub7cXqGUUsrlaIKolFLK6ewjcAUi0hpborgGWAcMxjYat9UYkycio4BOwABsI3V9RWRYqebGAW2BaOBeexslnTDG9AHeAR4zxhwC3gVeNcb0MsasrCDclsAlwDXA9FLPIw94Cphtb2t2qWtXAYOMMb2Br4DHy3sQY0yyvY1ewAfAXGPM4bLaqMRz+D/gCWNMD2Ab8HSJ+3yMMQOwjdA+zYVuBRbb4+gJbBaRpvaYxhtjemIb/QXYDQyzx/YU8HwZ7f0NWGKM6Q+MAGaISFB5r4NSSinXolNMlVJK1ZbiUcQhwCtAuP12Ov+b+jnK/hVn/7k+toRxRYl2LgG+McYUAcdEZGmpx4mxf9+ILZmsqnn2tneWNQJYgQhgtoi0BPyAgxVdICJDsSW6xaN7VWpDRIKBEGPMcvuhT4BvSpxS8vVoW0YTG4CZIuKL7blvFpHhwApjzEEAY8wp+7nBwCci0gkwgG8Z7Y0Crise3QQCgNbAros9D6WUUq5BRxCVUkrVltXYEsJobFNM12Ib/RuCLXkEEOCF4pE1Y0xHY8xHpdqRCh4n1/6EA7xiAAAgAElEQVS9kOp9EJpb4nZFj1XaG8Cbxpho4H5syVG57EngR8BEY0xmddqohIu+HsaYFcAwIAn41L7xjWBLAEv7F7DUGNMduLac2ATbyGPxv2FrY4wmh0op5SY0QVRKKVVbfsM2bfOUMabQPioVgi1JXGM/ZzFwj4jUBxCRcBFpVqqdVcB4+1rE5tg2b6lIBtDAAc+horaCsSVaAHderBH7iN3X2KaG7qlEG2U+rjEmHThdYn3h7cDy0uddJI42QIox5gNsyWofbP8el4lIO/s5jcuI7a5ymlyMbR2o2K/tXdlYlFJKWU8TRKWU8gAikiki7S0OYxu23UvXljqWbow5AWCM+Qn4AlgjItuAOVyYFM0FErGNQr6HbS1jegWPvQC4oaxNaqphKRBVvElNqfueAb4RkZXAiQraGQL0B/5ZYqOaMGAJsLKMNsp6Dv1EZBW2RHKGiGzFtnbz2co8Efua0Hhs6w7jgPHAa8aYVGAyECMiW7BNeW0NjAZeEJHfAO8y2ssEPsU29XSriGzHNupY+ry2ImJExMf+8yIRuWhCXR1i28houKPbdTT7RkerrI5DKaUAxJiyZpAopZQqSUQOAc2xTdPLxzZdcooxJsEB7d5rjPmlnPuHA58ZYyJq8jh1jYjUN8ZkikgosB4Yaow5ZnVctU1E7sLWfy4p5/5l2PrPh2XdX8PHrnbb9l1ZDwK+xpgCB8UzC0g0xvzdEe3Vpor+HZVSqjbpCKJSSlXetcaY+th2uTyOba2Y5YpHYeqiizy3hSKyGVgJ/MsTk0OllFLKGTRBVEqpKjLG5GCb+hhVfExE/EXkZRE5IiLHReRdEQm039dEbPX60kTklIistK+f+xTb7o4L7FNAzyuJYC8NsAgIs9+fKbbaec+IyBwR+UxEzgB32evTrbE/xlEReVNsNfuK2zIi0tF+e5aIvCUi34tIhthq7nUo7/mKyDcickxE0sVWL69bifsCReQ/InLYfv+qEs/7ErHVyksTkQT7KElxbb57S7Rx3vQ6e6wPicheYK/92Gv2Ns6IyEbgH/YNUKKwbazyVxHZb38+G0Wklf05/qfUc1kgIn8s4zm+KyIvlzr2nYj82X77CRH5f/buOzyqamvg8G+nkAQSCBB6Aon0EnoHQYoCKgiKCAqCWEAFsV+9n3IR9V4ueC1YUcSGggiIFBGk9xIIhBo6JKGXBAIJpOzvj31C2iQkJJOZJOt9nnmSOXPmnDWTI86avfdaUdbxw5VS3WwcI8h6rS7W/alKqbNpHp+ecm6lVBml1LfW3ypKKfWeUso1i/fjHuucMcr0f1yd9v2z9vlAKXVJKXVUKdXL2vY+pjLqZ9a185mNmDNO9VyllHpXKbXeeq1LlVJ+GffN6tgZrrP7lOmDeNn6243LeP40cdy8JpTpuxib5qaVNU00q2tRKfUM8BjwuvWcBdb2Y0qp7tbvHkqpj5VSJ63bx8r04EQpdZdSKlIp9YpS6qz1d3kim3iHKdMn84r1nj+W5rGnlVL7rMf2KqWaW9vfSHON7lVK9cvm+PWUUn8r8+9FuFJqQFb7CiFEfpMEUQghckkpVRJ4hPRr6f4L1MGs/6qFaeEw1nrsFcyauQqYaar/BLTWeghwAmtkUms9Me15tNZXgV7ASetxb6ufIMADmCTVF/gZM/X1Jcwav3ZAN+C5bF7GIOAdoCxwCHg/m30XY1pNVAS2W+dL8QHQArOerhym71+yMuvVFmNGWStY78uObM6RUV+gDalJ+FbrGOUwaxR/U0qlVNB82Xo99wKlgeHANUy7h0FpEjY/zPsyw8b5fgEeUepmYZWymHYNM5VpBj8KaKW19sGswzuW8QBWS4jLQEpRljuBWKVUfet+J1KLx/wAJGKulWbWudIlfWling28CZTHrBdsn2G3NtZ2P2Ai8K1SSmmt/w8zwjrKunZG2XjdtjwKPIH5e5cAXs24Qw6PfRV4HHON3gc8q5Tqe6uTa62bpFzvmL9tOOa6gyyuRa3119bvE63n9rZx6P8D2mKuoyaYXptpp6NWxhThqQY8CXxuXQfpKPPFzWSgl3U9tMe6tpVSD2PWoT6OuRb7ABespx7GXBNlMP/tTVemiq2t4/+NuSYrYq7tL1SaL2aEEMKeJEEUQoicm6eUisYkAXcDkwCspOJp4CWt9UWt9RVMA/GB1vMSMNNSa2itE7TWa3XeF4Bv1FrP01ona63jtNbbtNabtNaJVlP1KUDnbJ4/V2u9xVr/9TPmQ7NNWutpWusrWuvrmA+/TawRMBdMMjZGax1lVSbdYO33GLBMaz3Des0XtNa5SRD/Y72XcVYM061jJGqt/wd4AHWtfZ8C3tJah2tjp7XvFkzxmpTRvoHAKq31GRvnW4tp65BS/KU/5j0+iUm+PTCFady11se01oeziHs1pvpnZev+bFKrgZYGdipTebUX8KLW+qrW+izwEanXS1r3Anu01nOtv9VkION02uNa62+01kmYxLMK5ouI2/Wd1vqA9d7PIptrIzta61Va613WNRqGScyzuybTUUp1BN4D+mitL1vHtHkt5vCQjwHjtdZnrSI872AqvqZIsB5P0Fr/CcSSeo1llAw0Ukp5aa1Paa33WNufwiSpW61r8ZDW+rgV+29a65PW+/ErZnS8tY1j3w8c01p/Z13v2zGFmfrn8HUKIUSeSIIohBA511dr7YtJFkYBq61EoAJQEtimzBTDaOAvazuYRPIQsNSalvZGPsSSrjiOUqqOMtNYTysz7fTfmBGlrKRNMq5hGtJnopRyVUpNsKbGXSZ15MzPunliRkYyCshie05lfH2vWNP2Yqz3twypry+7c/0ADLZ+H4ypsJmJlbDPxIzWgBlFSxmdOgS8iElIziqlZipTbdSW1Zi2G52ANcAqTFLUGVirtU4GamCqfJ5Kc71MwYwWZVSVNO+FFWdkhn1Op3n8mvWrzb9nDuXo2rgVpVQbpdRKpdQ5pVQMMJLsr8m0zw3AJKdDtdUC5BbXYk5UBY6nuX/c2pbiQoaCOTZfuzWy/wjm9ZxSZqp2PevhLK9FpdTjylSgTfmbN8oi9hpAm5T9rH0fw4xwCiGE3UmCKIQQuWSNlM3FjCx1xLQiiAMaaq19rVsZa4oc1ojHK1rrOzDNxV9WqWvYbjWSmNXjGbd/CewHamutS2Omsea2ybstj2Kms3bHJGWB1naFed3xgK31ixFZbAcz9bBkmvu2PvjefH3KtHT4BzAAKGsl6TGkvr7szjUdeEAp1QSoD8zLYj8wI1z9lekL2AYzamOC0foXbSpM1rBi+28Wx1iNGYW8y/p9HdABkyCmTC+NwDSv90tzvZTWWtuaQngKuFnB1hqtzk1FW3uWKr/VsX8B5gMBWusywFfk4JpUZg3rPOBjrfXiNA9ldy3mJJ6TmL9fiurWtlzTWi/RWt+NGa3dD3xjPWTzWrSuqW8wXyyVt67h3dh+PyKA1WmuDV9r2uyztxOrEELkliSIQgiRS8p4ALN+b581KvQN8JGymror0+C9h/X7/UqpWtaH+8uYxDLJOtwZILv+hGeA8jmYRudjHTvWGs3Irw+TPphk5gImqft3ygPW654GfKhM8RxXpVQ7q/DHz0B3pdQAZYqalFdKpUxV3AE8qJQqqUxBkydzEEMicA5wU0qNxUzXTDEVeFcpVdv62zRWpv0FWutIzPrFn4A5KVNWbdFah1rnmAos0VpHAyil6iqlulqvKx7zZUBSFsc4aD0+GFhjTY08g+kvuNra5xSwFPifUqq0MgWLaiqlbE2/XAQEK6X6KlNI5nlyN5J0q+srL251bB/gotY6XinVGpPg5cQ0YL/OsCaXbK7FHMYzA3hLKVXBWts5FvMFQq4opSoppfpYawWvY6aiplwPU4FXlVItrGuxlpUclsIksOesYzyBGUG0ZSFQRyk1RCnlbt1aqdS1rEIIYVeSIAohRM4tUKYR+GVMUZehadYe/QMzjXSTNf1tGanrl2pb92OBjcAXWutV1mP/wXxojVZK2SoGsh/zwfaItU9WUxtfxXwAv4JJVn/N0ytN9SNmKl4UsJf0hXlSzrsLk4RdxIysuWitT2DWz71ibd+BKQwCZr3dDcwH+h9IX/TGliWY4iQHrFjiST8F9UPMdMSlmL/Nt4BXmsd/AILJYnppBjMwI1S/pNnmAUzAjJiexkwF/Wc2x1iNma54Is19BYSm2edxTAGYvcAlzFrFTAVLtNbngYcxxWcuYIr2hGASk5z4BDMqekkpNTmHz8mpWx37OWC8UuoKJhmblcPjDgT6qfSVTO/k1tfit5h1otFKKVsjxe9h3rswzDW73dqWWy6Y6/ok5trujFUQSmv9G+bfhl8w/y3OA8pprfcC/8P8938Gcz2ut3VwbdYw32O9Dycx19x/MdehEELYndJ5rpMghBBCOC+lVCfMSFGgNepZaClTGCgSeExrvdLR8QghhCh6ZARRCCFEkaWUcgfGAFMLa3KolOqhlPK1primrC3NOHomhBBC5AtJEIUQQhRJ1pqtaMzUzY8dHE5etMNUxjyPKXLUN7u1lEIIIUReyBRTIYQQQgghhBCAjCAKIYQQQgghhLC4OTqAguDn56cDAwMdHYYQQgghhBBCOMS2bdvOa60r3Gq/YpEgBgYGEhIS4ugwhBBCCCGEEMIhlFLHc7KfTDEVQgghhBBCCAFIgiiEEEIIIYQQwiIJohBCCCGEEEIIoJisQRRCCCGEEEI4XkJCApGRkcTHxzs6lCLL09MTf39/3N3db+v5kiAKIYQQQgghCkRkZCQ+Pj4EBgailHJ0OEWO1poLFy4QGRlJUFDQbR1DppgKIYQQQgghCkR8fDzly5eX5NBOlFKUL18+TyO0kiAKIYQQQghREMJmwUeNYJyv+Rk2y9EROYQkh/aV1/dXppgKIYQQQoiiIWwWLB8PMZFQxh+6jYXGAxwdlRE2Cxa8AAlx5n5MhLkPzhOjEEiCKIQQQgghioL8SsCSkyApAZITrJ9JaX5PTP2ZnABJiekfu/l4ymNpHl8+PjW2FAlxZrskiAXm2LFj3H///ezevTvfj71q1So++OADFi5cyPz589m7dy9vvPFGvp/H3iRBFEIIIYQQhV9WCdgfz8GGyRkSv8Sskzx0wcYdE1mw5ytk5oVGMWlJOCej46jq68VrPerSt1k1R4d1S3369KFPnz6ODuO2OF2CqJTqCXwCuAJTtdYTMjz+EdDFulsSqKi19i3YKIUQQgghhFPJKtFKSoDS1cDFDVzdwcXd+t3N/O5q3U/7eMpjN7elfa5rmv1u9dw05/imC1w+mTk+D2+4dhFKlrPv+1MIzQuN4s25u4hLSAIgKjqON+fuAshTkpiYmMjQoUMJDQ2lTp06/Pjjj3zwwQcsWLCAuLg42rdvz5QpU1BKMXnyZL766ivc3Nxo0KABM2fO5OrVq4wePZpdu3aRmJjIuHHjeOCBB9Kd4/vvvyckJITPPvuMYcOGUbp0aUJCQjh9+jQTJ06kf//+AEyaNIlZs2Zx/fp1+vXrxzvvvHPbryu/OFWCqJRyBT4H7gYiga1Kqfla670p+2itX0qz/2igWYEHKoQQQgghnMeJTVk/ViYAHv214GLJSvd30k+BBVCucP0KfNIUOoyGNs+ahLGYeGfBHvaevJzl46EnormRlJxuW1xCEq/PDmPGlhM2n9Ogamn+1bthtucNDw/n22+/pUOHDgwfPpwvvviCUaNGMXbsWACGDBnCwoUL6d27NxMmTODo0aN4eHgQHR0NwPvvv0/Xrl2ZNm0a0dHRtG7dmu7du2d7zlOnTrFu3Tr2799Pnz596N+/P0uXLuXgwYNs2bIFrTV9+vRhzZo1dOrUKdtj2ZuzVTFtDRzSWh/RWt8AZgIPZLP/IGBGgUQmhBBCCCGcz76F8OMDUKoCuHmmf8zdyxSqcQaNB0DvySZhRZmf/b6CZzdAYEdY8R580gQ2fQkJ0kQeyJQc3mp7TgUEBNChQwcABg8ezLp161i5ciVt2rQhODiYFStWsGfPHgAaN27MY489xvTp03FzM2NrS5cuZcKECTRt2pS77rqL+Ph4TpywnbCm6Nu3Ly4uLjRo0IAzZ87cPM7SpUtp1qwZzZs3Z//+/Rw8eDBPry0/ONUIIlANiEhzPxJoY2tHpVQNIAhYkcXjzwDPAFSvXj1/oxRCCCGEEI4XMg0WvQJVm8Gjs+DwCuetYgomFlvxDPoFIrbC8nfgrzdgw2dw1xvQZJCZplpE3Wqkr8OEFURFx2XaXs3Xi19HtLvt82ZsA6GU4rnnniMkJISAgADGjRt3s4/gokWLWLNmDfPnz+fdd99lz549aK2ZM2cOdevWTXeclMTPFg8Pj5u/a61v/nzzzTcZMWLEbb8We3C2EURbTTuyWik8EJittU6y9aDW+mutdUutdcsKFSrkW4BCCCGEEMLBtIaV/4GFL0HNbjB0AZTyM8nXS7thXLT56UzJ4a0EtIJhC+HxP8CnEswfBV+0hT2/Q3LeRswKq9d61MXL3TXdNi93V17rUTeLZ+TMiRMn2LhxIwAzZsygY8eOAPj5+REbG8vs2bMBSE5OJiIigi5dujBx4kSio6OJjY2lR48efPrppzcTvdDQ0NuKo0ePHkybNo3Y2FgAoqKiOHv2bJ5eW35wtq8kIoGANPf9ARureQGTID5v94iEEEIIIYTzSEqEP1+Bbd9D08eg9yemCExRccddENQZ9i8y005/GwaVG5vR0FrdoRg1mU8pRJPfVUzr16/PDz/8wIgRI6hduzbPPvssly5dIjg4mMDAQFq1agVAUlISgwcPJiYmBq01L730Er6+vrz99tu8+OKLNG7cGK01gYGBLFy4MNdx3HPPPezbt4927cxoqLe3N9OnT6dixYp5en15pVIyX2eglHIDDgDdgChgK/Co1npPhv3qAkuAIJ2DF9CyZUsdEhJih4iFEEIIIUSBSYiD2U9C+CLo+LJJmopywpScBLt+g5X/hujjUL29ec01bn96paPt27eP+vXrOzqMIs/W+6yU2qa1bnmr5zrVFFOtdSIwCpP87QNmaa33KKXGK6XSNhIZBMzMSXIohBBCCCGKgGsX4ce+EP4n9JoI3f9VtJNDMC01mgyEUSFw3//g4hH4ridM7w+ndjo6OlFEOdsUU7TWfwJ/Ztg2NsP9cQUZkxBCCCGEcKCYSJj+kEmQ+k+DRg86OqKC5VYCWj0FTR6FLV/Duo9gSido2A+6/B/41XZ0hKIIcaoRRCGEEEIIIdI5uw+m3m2azA+eU/ySw7RKlISOL8KLYdDpdTiwFD5vA3+MguiIWz9fiByQBFEIIYQQQjin4xtgWg/QyfDEnxDk2AbiTsOzDHT9PxizE9qMgLBf4dPmsPgNiD3n6OhEIScJohBCCCGEcD77Fpo1h6UqwJNLoXKwoyNyPt4VoOd/YPR2s1Zxy9fwSRNT/TQ+xtHRiUJKEkQhhBBCCOFctn4Ls4aYpHD4Uihbw9EROTffAOjzKTy/GercA2smwceNYd3HcOOao6MThYwkiEIIIYQQwjlobVo6LHrZ9PwbOh9KlXd0VIWHX214+HsYsQYCWsOyf8HkprDlG0i84ejonMKxY8do1KhRjvf//vvvOXkyq7bsqfuMGjUqr6E5DUkQhRBCCCGE4yUlwoIxsPq/0PQxGPgLlCjl6KgKpypN4LHf4Im/oFxN+PNV+Kwl7JxpeisWJmGz4KNGMM7X/AybVaCnz0mCaC+JiYkOOa/TtbkQQgghhBDFzI1rMOdJ0+Pwzleh61tFv8dhQajRzhT3ObQclr8Dv48w0067vgX17nP+9zhsFix4ARLizP2YCHMfoPGA2z5sYmIiQ4cOJTQ0lDp16vDjjz/ywQcfsGDBAuLi4mjfvj1Tpkxhzpw5hISE8Nhjj+Hl5cXGjRvZvXs3Y8aM4erVq3h4eLB8+XIATp48Sc+ePTl8+DD9+vVj4sSJAHh7ezNmzBgWLlyIl5cXf/zxB5UqVeL48eMMHz6cc+fOUaFCBb777juqV6/OsGHDKFeuHKGhoTRv3hwfHx+OHj3KqVOnOHDgAB9++CGbNm1i8eLFVKtWjQULFuDu7p6ntzkjVRx6zbds2VKHhIQ4OgwhhBBCCJHRtYswYyBEbIFeE6HNM46OqGhKToZ9f8CK9+HCQajaHLqNhZpdCjSMffv2Ub9+fXNn8RtwelfWO0duhaTrmbe7eoB/K9vPqRwMvSZkechjx44RFBTEunXr6NChA8OHD6dBgwYMHz6ccuXKATBkyBAGDBhA7969ueuuu/jggw9o2bIlN27coF69evz666+0atWKy5cvU7JkSaZPn8748eMJDQ3Fw8ODunXrsm7dOgICAlBKMX/+fHr37s3rr79O6dKleeutt+jduzf9+/dn6NChTJs2jfnz5zNv3jyGDRvG+fPn+eOPP3B1dWXcuHEsW7aMlStXsnfvXtq1a8ecOXPo1asX/fr1Y+jQofTt2zf799milNqmtW6Z9RtuyBRTIYQQQgjhGNERMK0nnAw1a+ckObQfFxdo2A+e2wQPfA5Xz8FPfeGH3hCx1ezj4OmcmdhKDrPbnkMBAQF06NABgMGDB7Nu3TpWrlxJmzZtCA4OZsWKFezZsyfT88LDw6lSpQqtWpnktHTp0ri5mQmZ3bp1o0yZMnh6etKgQQOOHz8OQIkSJbj//vsBaNGiBceOHQNg48aNPProo4BJSNetW3fzPA8//DCurq437/fq1Qt3d3eCg4NJSkqiZ8+eAAQHB988Xn6SKaZCCCGEEKLgndkL0x+CG7EweC4E3enoiIoHVzdoNhiCH4aQ72DtB/Btd6jcFM7vg0Qr+cqn6ZzZymakDzBJakxE5u1lAuCJRbd9WpVhaq1Siueee46QkBACAgIYN24c8fHxmZ6ntc703BQeHh43f3d1db25ftDd3f3mc9Juzy6mUqXSr71NObaLi0u647m4uNhlnaKMIAohhBBCiIJ1bD181xN0MjyxWJJDR3DzgLYj4YUd0PVtOL0zNTlMkRAHy8c7Jj4wU2DdvdJvc/cy2/PgxIkTbNy4EYAZM2bQsWNHAPz8/IiNjWX27Nk39/Xx8eHKlSsA1KtXj5MnT7J1qxlxvXLlym0naO3bt2fmzJkA/PzzzzdjcAYygiiEEEIIIQrOvgUw+0nwrQ5D5pqfwnE8vKHTq7DiPduPx0QWbDxppYxcLh9v4ijjb5LDPI5o1q9fnx9++IERI0ZQu3Ztnn32WS5dukRwcDCBgYE3p5ACDBs2jJEjR94sUvPrr78yevRo4uLi8PLyYtmyZbcVw+TJkxk+fDiTJk26WaTGWUiRGiGEEEIIUTC2fmtaLlRrAY/OgpLlHB2RSJHddM6XdufbaWwVTxH5T4rUCCGEEEII56W1qZ656GWodTc8/ockh87G1nROgKrNzN9PFBuSIAohhBBCCPtJSjTFTtZMNMVRBv4CJUrd+nmiYDUeAL0nmxFDlJnOGdAO9s03PSpTehGKIk/WIAohhBBCCPu4cQ1mD4cDi6HTa9Dl/5y/OXtx1nhA+vV9WsP6T2DZOLh41CT3pavk+TTZVQMVeZfXJYQygiiEEEIIIfLftYvw4wNw4C+49wPo+pYkh4WNUtDxRZMYnguHb7rCyR15OqSnpycXLlzIcxIjbNNac+HCBTw9PW/7GFKkRgghhBBC5K/oCNPj8NJReGgqNHjA0RGJvDq9G2YMhKvnod9X0LDvbR0mISGByMhIm30GRf7w9PTE398fd3f3dNtzWqRGppgKIYQQQoj8c2aPSQ5vXIMhv0Og8/R3E3lQuRE8vQJ+HQy/DYXz/2emDedyVNjd3Z2goCA7BSnyg0wxFUIIIYQQ+ePYepjWy/w+fLEkh0WNd0V4fD40Hggr35fiNUWUJIhCCCGEECLv9v4BP/UDn0rw5FKo1NDREQl7cPc0U0y7j4Pdc+G7e+HKaUdHJfKRJIhCCCGEECJvtk6FWUOhShMYvgR8qzs6ImFPSkHHl2Dgz6Z4zddd8ly8RjgPSRCFEEIIIcTt0RpWvAeLXoE6PeDxP6BkOUdHJQpKvfvgySXg4grTeppRZFHoSYIohBBCCCFyLykR5o+GNZOg2RB45GcoUdLRUYmCVjnYFK+pHAyzHofVk8wXB6LQkiqmQgghhBDOIGwWLB8PMZFQxh+6jU3ftNwZpI3RzQMS46HT69Dln9LjsDjzrghDF8CCF2Dle3BuPzzwGbh7OToycRskQRRCCCGEcLSwWebDdUpFyJgIcx+cJ0nMGGNiPLi6g19tSQ6FVbxmClSoa75EuHQUBv4CPpUdHZnIJUkQhRBCCCEcbfn4zO0CEuJg8RuAAp0EOtncklN+TzJT+TJtS9kv2ca2NL/b3JaU4blptoX/BYkZYkxKMLE7SxIrHEspuPMV8KsDc5+Bb7rCoBmmeJEoNCRBFEIIIYRwtJhI29vjLsDcp/LhBMoUElEuoFJ+uoCLS/ptLmkeu7mPtS1jcnir2EXxVb+3qWY7Y5ApXtNvCjTo4+ioRA5JgiiEEEII4UgXDqeu58vIuzIMW5g5WcuU1KkMiV/aZFDlzxTQjxqZqa8ZlfHP+7FF0VOlsSleM/NRmDUEur4Fd74q05ELAaerYqqU6qmUCldKHVJKvZHFPgOUUnuVUnuUUr8UdIxCCCGEEHl2/Qr8PRY+b2Omirq4p3/c3Qvuedes8StfE8oFmf6CZfyhdFXTkN67gmkr4VUWPEuDh7epJOrmYdYHurjk3wfybmMzFx1x9zLbhbDFpxIMWwTBA0w7lLlPZ55KLZyOU40gKqVcgc+Bu4FIYKtSar7Wem+afWoDbwIdtNaXlFIVHROtEEIIIcRtSE6GsJmwbBzEnoEmj0L3f8HRNc5dxTQlFmeOUTgfd0948GtTvGbFu3AxpXhNJUdHJrKgtBP1KVFKtQPGaa17WPffBNBa/yfNPhOBA1rrqTk9bq2OzqwAACAASURBVMuWLXVISEh+hyuEEEIIkTuRIbD4dYjaBtVaQK+J4N/S0VEJUTD2LTDFa7zKSvEaB1BKbdNa3/IfHGebYloNSDu5PdLallYdoI5Sar1SapNSqqetAymlnlFKhSilQs6dO2encIUQQgghcuDKafh9JEztZkbf+n4FTy6T5FAUL/V7w/C/zO/TesLe+Y6NR9jkbAmirUnyGYc43YDawF3AIGCqUso305O0/lpr3VJr3bJChQr5HqgQQgghxC0lXoe1H8KnLWD3HOjwIozeBk0HmfWBQhQ3VZrA0yuhYgNTvGbNJLMGVzgNp1qDiBkxDEhz3x84aWOfTVrrBOCoUiockzBuLZgQhRBCCCFuQWsIXwxL/mkahte9F+55zxSbEaK4SyleM3+UKV5z7gD0+dSsVxQO52xfXW0FaiulgpRSJYCBQMax53lAFwCllB9myumRAo1SCCGEECIrZ/fD9Adh5iBwLQGD55r1VpIcCpHK3RMe/Aa6vg27ZsH398GVM46OSuBkCaLWOhEYBSwB9gGztNZ7lFLjlVIp3TWXABeUUnuBlcBrWusLjolYCCGEEMISFw2L34Av20PkNug5AZ5dD7W6OToyIZyTUtDpVRjwE5zdC990hVNhjo6q2HOqKqb2IlVMhRBCCGE3yUmw/UdTwv/aRWgxzDQFL+Xn6MiEKDxO7YQZgyDukmmLUb+3oyMqcgprFVMhhBBCiMLj2Hr4ujMsfBH86sKINdD7Y0kOhcitKk3g6RWmeM2vg2Ht/6R4jYM4W5EaIYQQQgjnFx0Bf78Ne36H0v7Q/zto2M9MmRNC3B6fyjBsIfwxCpaPh3Ph0HuyFK8pYJIgCiGEEELk1I1rsGEyrPsY0ND5DegwBkqUdHRkQhQN7l7w0FSoWM9UOL14BAb+At4VHR1ZsSEJohBCCCHErWhtRgv/HgsxEWa08O7x4Fvd0ZEJUfQoBZ1eA7868PtI+LoLPDoTKgc7OrJiQRJEIYQQQojsnN5lqpMeXweVgqHfVxDY0dFRCVH0NXgAfGuY4jXf9oAWQ2HfAoiJhDL+0G0sNB7g6ChvmhcaxaQl4ZyMjqOqrxev9ahL32bVHB1WrkmCKIQQQghhy9ULsPI92PY9ePrCfR+aCqUuro6OTIjio2pTeGYlfHsPbPoidXtMBCx4wfzuBEnivNAo3py7i7iEJACiouN4c+4ugEKXJNqliqlSqpw9jiuEEEIIYXdJCbDpK/i0GWz7AVo/Ay9sh1ZPSnIohCP4VDbtZDJKiDPFbJzApCXhN5PDFHEJSUxaEu6giG6fvUYQNyuldgDfAYt1cWi2KIQQQojC7/BK+OsNOLcf7rjLNLuvWN/RUQkhLkfZ3h4TAQnxDq90ejI6LlfbnZm9+iDWAb4GhgCHlFL/VkrVsdO5hBBCCCHy5uIRmPEo/NQXEuNN1cQh8yQ5FMJZlPHP+rGPg2HNBxB3qeDiycDH0/a4W1VfrwKOJO/skiBq42+t9SDgKWAosEUptVop1c4e5xRCCCGEyLXrsbDsHfi8DRxZBd3+Bc9vgXr3SU9DIZxJt7GmBUZa7l7Q8WWo0hhWvAsfNoS/3jR9SgvQ9hOXuBKfiGuGfzK83F15rUfdAo0lP9hliqlSqjwwGDOCeAYYDcwHmgK/AUH2OK8QQgghRJbCZpn1SjGRUKYa1L4H9v8Jsaeh8UDoPg5KV3F0lEIIW1IK0dz8bzhDFdPTu2HDp7Dla9g8BRo9BB1esHtrjJi4BF6YEUpVXy+e71qTz1ccLvRVTJU9lgcqpQ4APwHfaa0jMzz2D631f/P9pNlo2bKlDgkJKchTCiGEEMKZhM0yFQ8TMqwH8q0BD30LAa0cE5cQIn/FRMKmL0314RuxULMrdBgDQZ3zfVaA1ppRM0L5a/dpZo1oR4saZfP1+PlNKbVNa93yVvvZq0hN3awK0xR0ciiEEEKIYi7xBiz5Z+bkEEAnSXIoRFFSxh96vA+dXoWQ72DzV/DjA1C5sUkUG/QF1/xJgX7dGsGisFO83rOu0yeHuWGvIjVLlVK+KXeUUmWVUkvsdC4hhBBCiPQuHYet35rCMxOD4Oo52/vFZFEZUQhRuHmVhTtfhhd3QZ9PzRdEc5407Ws2fQU3rubp8AfPXGHcgj10rOXHyE418ylo52CvEcQKWuvolDta60tKqYp2OpcQQgghiruEODi2Hg4tM7cLB832MtXNGqW98+Ha+czPy64yoshkXmgUk5aEF/o1VqIYcfOA5o9D08FwYDGsnwx//QNWT4BWT5s+p94VcnXI+IQkRv0SSqkSbnw4oAkuLkWroJW9EsQkpVR1rfUJAKVUDUB6IQohhBAif2gN5w+mJoTH15v2FG6eENjRNLWv1R3K1zLrjqq3y7wG0d3LFLkQOTIvNIo35+662Qw8KjqON+fuApAkUTg/FxdTnbjefXBiM2yYDGsmmZ9NH4V2o6B8zkYC31u0l/AzV/j+iVZULO3Y/ov2YK8E8f+AdUqp1db9TsAzdjqXEEIIIYqD+MtwdI2VFC6HmBNmu18daDkcanWDGh0yl8KHW1dAFLc0aUn4zeQwRVxCEpOWhEuCKAqX6m2g+s/mS6YNn0LodLNesX5vs07RP+s6Ln/tPsX0TSd4+s4g7qpbNCdI2qWKKYBSyg9oCyhgo9baxryOgiFVTIUQQohCSGs4vSs1IYzYBMmJUMIH7uhsEsKa3aBsDUdHWuTtP32Znh+vtfmYAo5OuK9gAxIiP105A1umwNapEB9jvmhq/4JpheOSWrIlKjqOXh+vIdCvFLNHtqeEm73KudiHo6uYAiQBZwFPoIFSCq31GjueTwghhBCF3bWLcHiFSQgPL4fYM2Z75WBoP9pMG/VvDW4lHBtnMbH9xCW+WHmIZfvOorC9Xqiqb9GbYieKGZ9KZkZBx5dg+0+w8XOY8QhUqGf+3Ql+mETlzpgZoSQlayYPbFboksPcsEuCqJR6ChgD+AM7MCOJG4Gu9jifEEIIIQqp5CSI2p66ljBqG6BNBcKa3axRwq7gU9nRkRYbWms2HL7A5ysPseHwBXxLuvPy3XXw8y7Buwv3ZZpmWqWMJ3E3kvAq4eqgiIXIJx4+0O45aP007J5r1if+8TyseI8N5foTfrwJ/x7YnkC/Uo6O1K7sMsVUKbULaAVs0lo3VUrVA97RWj+S7yfLAZliKoQQQjiRK6fNCOGhZWa0MD4alAtUa2lGCGt1h6pNwUUSjoKUnKxZvv8sn608xM6IaCr6ePBMpzsY1Lo6pTzMmELaKqZVfD1pXM2XJXtPU69yaaYMbkH18iUd/CqEyEdaw+EVRC/7AN/TG4h3KYln26eg7bNQuqqjo8u1nE4xtVeCuFVr3UoptQNoo7W+rpTaobVumu8nywFJEIUQQogCEDbLdhGYxBtm/eChZXBoBZwxlS/xrmwlhN3gjrugZDlHRl9sJSYls2jXKb5YeZjwM1cIKOfFyM416d/CHw+3WyfpK/efZczMUJRSfDKwaZEt3CGKp4tXb9DrkzU0dTvO5zXW4bZ/HihX829b+9FQsb6jQ8wxRyeIvwNPAC9ippVeAty11vfm+8lyQBJEIYQQws7CZmVuI+HqDhUawMXDcCMWXNyhetvUUcJKDU0LCuEQ1xOTmLs9iq9WH+b4hWvUrujN811qcX/jKri55m591fELVxnx0zbCz1zh5e51eL5LrSLXG04UP1prnvohhLUHz/P78+1pWLUMXDoGG7+A0J8g4RrU7gEdXjCFbXb95tSVkh2aIGYIpDNQBvhLa33DrifLgiSIQgghhJ191AhiIjJvV67QYphJCIPuNGt8hENdu5HIjC0RfLPmCKcvx9PYvwzPd6nF3fUr5Smpi7uRxBtzw/hjx0m616/Eh480obSnez5GLkTBmrbuKOMX7uVfvRvwRIeg9A9eu2iqnm6eAtfOg28gXDkJSWnSHXcv6D3ZaZJEhyWISikXIExr3ShfD5wHkiAKIYQQdnR6N3zVIYsHFYyLLtBwhG0xcQn8uOEY09Yf5dK1BNreUY7nu9SiYy0/VD6N5Gqt+X7DMd5ftI+AciX5anAL6laWLwVE4bM7KoYHv9hApzp+fPN4y6z/G0mIgx2/wOLXTNGtjMoEwEu77RtsDjmszYXWOlkptVMpVV1rfSK/jy+EEEIIJ5CcBOF/mm/Pj62FrJoglPEv6MhEBueuXGfa+qP8tPE4sdcT6VqvIs93qUmLGvm/5lMpxRMdgmhYtQzP/bydfl+sZ2L/xtzfuPAV9BDF19XriYyeEUrZUu5M7N8k+y9Q3L2g1ZOw6BXbj8dE2idIO7JXH8QqwB6l1BbgaspGrXUfO51PCCGEEAUh7pLpE7blG4g5Yb4dv3u8mTq65J/p1yC6e5k1OMIhoqLj+Hr1YWZujeBGUjL3BVfh2btqmnVUdtY6qByLXujIcz9vZ9QvoeyMiOYfPevlem2jEI4w9o89HL9wlV+ebku5UjnsuVrG3/Y0+0L4JZm9EsR37HRcIYQQQjjC2f2w+SsI+9UUZqjREXq8D3XvBVfr40QJb6cu0FBcHDkXy5erDvN7aBQADzavxsjONbmjgneBxlGptCcznm7Le4v28s3ao+yKiuGzR5vj5+1RoHEIkRvzQqOYsz2SF7rVpu0d5XP+xG5jMxfqKqRfktm9SE1uKaV6Ap8ArsBUrfWEDI8PAyYBUdamz7TWU7M7pqxBFEIIIW5DcjIcXGISwyOrwNUDGj8MrUdAlcaOjk5ksOdkDF+sOsyfu05RwtWFQa2r83SnO6jm6+Xo0JizLZJ//r6LcqVK8MVjzWlWvayjQxIik2Pnr3Lf5LU0rFqGX55uk/sR76xa/TgJR7e5uELqQoQSgDtwVWtd+hbPcwUOAHcDkcBWYJDWem+afYYBLbXWo3IajySIQgghRC7Ex0Doz7Dla7h0FHyqmjU2LZ6AUrn4Rl0UiG3HL/LZikOsDD+Hj4cbQ9rVYHjHIKcbqdsdFcPI6ds4e/k64/o0ZFDrgHwrjiNEXt1ITOahLzdw4uI1Fo+5k6pO8MVKfnNYkRoArXW6clVKqb5A6xw8tTVwSGt9xHreTOABYG+2zxJCCCHyg5N/+2t35w+apHDHL6ZvYUAb8x7U7216GgqnobVm7cHzfL7yEJuPXqRcqRK8ek8dhrQLpIyXc/6tGlUrw8LRHXlh5g7++fsudkRcYvwDjfB0d3V0aEIw8a/97IqKYcqQFkUyOcwNe61BTEdrPU8p9UYOdq0GpF3dGQm0sbHfQ0qpTpjRxpe01plWhCqlngGeAahevXrugxZCCFG8ZGz0HhNh7kPRThKTk+HwCtj8JRxaBq4loNFD0PoZqNbc0dGJDJKTNUv3nuGLVYcIi4yhcmlP3r6/AYNaB1CyRIF8rMsT35Il+G5YKz5edoBPVxxi/+krfDm4hVNMgxXF18rws0xdd5QhbWvQo2FlR4fjcPaaYvpgmrsuQEugs9a63S2e9zDQQ2v9lHV/CNBaaz06zT7lgVit9XWl1EhggNa6a3bHlSmmQgghbimrRu9O1MMqX12/AjtmwJYpcOEQeFeClk9CyyfAu6KjoxMZJCYlsyDsJF+sPMzBs7HUKF+SZzvXpF/zani4Fc4RuKV7TvPKrJ24u7nw6aBmdKjl5+iQRDF09nI8vT5ZSwUfD+Y936FIj2g7dIop0DvN74nAMcxU0VuJBALS3PcHTqbdQWt9Ic3db4D/3l6IQgghRBpZ9aqKiYAFL0KVJuZWqSG4Odfarly5eMS0qAidDtcvQ9Xm8OA30KAvuOWwnLuwi3mhUUxaEs7J6Diq+nrxWo+69GxUmdnbIpmy5jARF+OoW8mHTwY25b7gKoW+ZcQ9DSvzxyhvRvy0jSHfbua1HvUY2fkOWZcoCkxysualWTu4diOJzx5tVqSTw9xwqiqmSik3zLTRbpgqpVuBR7XWe9LsU0Vrfcr6vR/wD6112+yOKyOIQgghsqU1/DcQ4qMzP+bqAe6epnALgIsbVKxvJYxNza1SQyhRskBDzhWtTRXSzVPgwF/g4moSwrbPgv8tv0wWBWBeaBRvzt1FXELSzW1uLgovdxeuXE+iSYAvo7rUolu9iri4FK0E6ur1RF6fE8aisFP0alSZSQ83wdvD+afLisLv85WHmLQknP8+FMwjrYr+kjSHjiAqpX4Axmito637ZYH/aa2HZ/c8rXWiUmoUsATT5mKa1nqPUmo8EKK1ng+8oJTqgxmZvAgMs8drEEIIUUxcj4X5o01yqFxBp35Ax90Lek+G4Ich+jic3AGndppb+GIzCgfmeRXqpo4yVmkKlYPBo2D7zmVy46rpW7h5CpzbDyX9oNNr0HI4lK7i2NhEOpOWhKdLDgESkzU3kjQ/P9WG9jXLF9mRtVIebnw2qBlN/X2Z8Nd+Dny2jilDWlKrooP/+xFF2rbjl/jw7wPc37gKA1oG3PoJxYi91iCGaq2b3WpbQZERRCGEEDadOwCzhsD5A9D1LSjtDyvezVkVU63hcpRJFm8mjjsg9oy1g4LytaBq0zSjjY3Bs4z9X9el47D1G9j+oxn5rNzYjBY2fNCMhgqnE/TGImx9IlPA0Qn3FXQ4DrPh8HlG/xLK9cRkPni4CT0bScEQkf9i4hK495O1uLjAohfupLSnc1b+zW+OXoPoopQqq7W+ZAVTzo7nEkIIIXJvzzz443lw84Qhv8Mdd5ntTR7J2fOVMklkGX+ol+YD/JXT6ZPG4xtg12+pj5cNSpM0WoljyXJ5fz1aw7F1pql9+J+AMu0p2oyE6m1NvMJpVfX1Iio6zub24qR9TT8WjO7Isz9vZ+T0bTx7V01evacurkVsWq1wHK01b84N48zleH4b2a7YJIe5Ya+k7X/ABqXUbEADA4D37XQuIYQQIueSEmHZv2DjZ1CtJQz4EcpUy7/j+1Q2tzo9UrfFnoPTaZLGqO2w5/fUx8tUN6OLVa01jVWa2K4kaqtPY/3eJgHdPAXO7AavstBhDLR6yuwjCoWnOwUxbn76ts9e7q681qOugyJynKq+Xswa0ZZ3Fuzly1WH2RUZw+RBzShXSoooibybsSWCP3ed5h8969GsellHh+OU7FakRinVAOiKmR2xXGvtsGb3MsVUCCEEAFfOwOwn4Ph6k0D1+LfjKpJeuwinw9Kva7x4OPVxn6omUUwZbYw+YRLbhDSjTC5upohOwlWo2BDajjTrJd2L16hTYZeYlMyjUzcTeuISZUuW4NyV6zermPZtlo9fXhRCv249wdt/7KGCtwdfDW5BsH8BTNEWRdaBM1fo/ek6WgeV44cnWhe5gk+3ktMppvZag9gW2KO1vmLd9wEaaK035/vJckASxFyy9Q11UW4SLYQoHo5vhN+GmTV5vT/J+VTSghQfA6d3pU8azx8Am6vTLG5e8NhvENhRppEWUh/9fYBPlh/kwwFNeLC5jPpmFBYZzciftnH+6g3e69tICoqI2xKfkESfz9Zx8eoN/hxzJxV9it96bEevQfwSaJ7m/lUb24QzCpsFC15I/YY6JsLcB0kShRCFk9ZmXd7St8C3OgyeA5UbOToq2zzLmEQvsGPqtuuxZurotB62n5MYD0F3Fkx8It9tPHyBT1cc5MHm1SQ5zEJjf18WjO7ICzNDeX12GDsjohnbuwEebtKzTuTcuwv3cuBMLD8Mb10sk8PcsFeHVaXTDE1qrZORIjWFw/Lx6acvgbn/97/MhywhhChMrsfC7OHw1xtQuwc8vdJ5k8OseHibIjNlshg1kXWGhdaF2Ou8+GsogeVL8e4Dhey6LGDlvT344YnWjOxck583n+CRKZs4FZO5qI8QtizedYqfN59gRKc76FyngqPDcXr2ShCPKKVeUEq5W7cxwBE7nUvkl6QEM2Joy5WT8N8aMPVuU/Vv/WQI/wsuHoHkJNvPEUIIRzp3AKZ2g73zzFT5R6aDl6+jo7p93cZmXlvo7mW2i0JHa82rv+3k0tUEPn20GaWkMfwtubm68Eavenz5WHMOWmvJNh6+4OiwhJOLvHSNf8wJo4l/GV65p/gVfbod9vrXaCQwGXgLs3BiOfCMnc4l8kpr2L/QjBJmxdMXGj1oPnAdWJLaHBpMgQS/2tatLlSoY36WryX9toQQjpFVC4vCLGWav6wRLxK+XXeUleHneKdPQxpWlcIrudEruAq1K3nzzE/bGPztZt7sVY8nOwahZA2uyCAxKZkxM3eQrOHTQc0p4WavsbGixW5VTJ2JFKnJRmQILPk/iNgEFepBrbshZGr6aabuXtB7cvoPIdcuwvmDcD4czoWbIgrnD5jmzDeLKSgoWyN90uhXx/zuJWWFhRB2YO8WFkLkg50R0fT/agNd6lZkypAWktjcpivxCbz6206W7DnD/Y2rMLF/Y0qWkJFYkeqDJeF8tvIQnwxsygNN5f8FDi1So5TyBJ4EGgI3h5C01sPtcT5xGy4eheXvmD5cpSrC/R9DsyHg6mZ6cd3qG+qS5aB6G3NLKyEOLhyyksaUBPIAHFkFSddT9ytVESrUzTzqWLrqravwSZVVIYQtztTCQogsXI5PYPSMUCr6eDKxf2NJDvPAx9Odrwa34KvVR5i0ZD8Hz8TSv4U/3284xsnoOGkVUsxtOHSez1cd4uEW/pIc5pK92lz8BuwHHgXGA48B+7TWY/L9ZDkgI4hpXLsIaz6ALV+Dqzu0H21uHj72PW9yElw6lj5pTPl5PSZ1vxLemZNGvzpQLsjEm7HKKtge4RRCFC+FoYWFKPa01oyeEcri3af59Zm2tAws5+iQioy1B88x4scQriUkp9vu5e7Kfx4MliSxmLkQe51en6zF29ONhaM7ysiyxdFtLmpprR9WSj2gtf5BKfULsMRO5xI5kXjdJIVrJkH8ZWg2GLr8H5SuUjDnd3GF8jXNrW7P1O1aQ+zZzFNVj66BsJlpnu8O5e4wjaITbVRZXT5eEkQhiqPC1MJCFHuzQiJYGHaK13rUleQwn91ZuwI+Xu5cS7iebntcQhKTloRLgliMaK15bXYY0XEJfPdEK0kOb4O93rEE62e0UqoRcBoItNO5RHa0ht1zTAIVfRxqdYe7x0Olho6OzFAKfCqZW1Cn9I/FX7ZGHA+kH3W0JSYCYqJkrZEQxcn1WJg/GvbMhbr3Qd8vCneVUlGkHThzhX/N30PHWn4827mmo8Mpks5evm5z+8loaYdRnExbf4wV+89KAag8sFeC+LVSqiymiul8wBt4207nElk5vsF8qx61DSoFm0p+Nbs6Oqqc8ywN/i3MLcVHjbJuxfFRA1M5Nagz3NEZAu80ayWFEEXPuQMwa4j5AqnbWOjwErhIdTrhnOITkhj1y3a8Pdz48JEmuLjIukN7qOrrRZSNZLCqr5eNvUVRtDsqhgmL93F3g0o83q6Go8MptOySIGqtp1q/rgHusMc5RDbOHzJV/PYvBJ+q0PdLaPyImeZZ2HUba3sNYqfXTLuNo6th50wI+RZQUKWJSRaDOkP1dlCipMNCF6JAFIciTkWxhYUo0t5ZsJcDZ2L5YXhrKvpI+yd7ea1HXd6cu4u4hNT+zF7urrzWQ3rfFQex1xMZPSMUP28PJj4kBaDyQiblFiVXz8OqCbDtO/PBqevb0Pa5opUU3aoPWPtRkJRgRk2PrDbVUzd+Aes/AdcS4N/afJi8ozNUbW6qtgrnUhwSHHvJWMQpJsLch6LxHkoLC1EILQw7yYwtJxjZuSad61RwdDhFWso6w0lLwomKjsPNRfHvfo1k/WExMXbebo5fuMqMp9tStlQJR4dTqEkfxKIgIQ42fQFrP4KEa9BiGNz1BnhXdHRkzuHGVVPh8OgqkzSeDjPbS/hAYIfUKakVG9y6xYawL6lSmzdZTcEuEwAv7S74ePKTtLAQhVDExWvc+8laalXyZtaIdri7yjTogjJrawSvzwlj9sh2UhCoGJi7PZKXZ+1kTLfavHR3HUeH47QcXcVUFITkZAj7FVa8C5ejoO690P0d0x5CpCpRCmp3NzeAqxfg2BqTLB5dDQf+MttLVUhNFoM6Q1mZu17glo9PnxyCVKnNjtamfUzkVojYnPX63JgI2D3XrMv1LoQjGGlbWPT7WlpYiELhRmIyo2aEgoLJA5tJcljA7mtchXEL9jArJEISxCLu6PmrvDVvN60DyzG6ay1Hh1Mk2CVBVEo9aGNzDLBLa33WHucsdo6sMgVoTu+Cqs3gwa8hsKOjoyocSpWHhv3MDSA6wiSKKQnj7tlme9nA9AljKT+HhVzkxcfA0bXZJzibv4aqTaFSo6I1bTo3EuLg5A6I3AIR1u2q9U9qCW+zDjfJVhU/ZUbfACrUh6A7TbIY2NG5CzlJCwtRiP1vaTg7I6L58rHmBJQrpv9mOVApDzfub1yFhWGnGNu7Id4eMiZSFF1PTGL0jO2UcHPh44FNcZMvYvKFvf5reRJoB6y07t8FbALqKKXGa61/stN5i74ze+HvsXDobyhTHR76Fho+KNX78sI3wPSFbDbYfCA9tz81WdzzO2z/wexXKTg1WazRHjy8HRt3YZaUaNaJHl4BR1ZCZAjoJEABNqa9KxdY/Frq7351oEpTU4SoalOoHAwePgX5CgpGTFT6ZPDUTki2ugiVDTJViQNam1vFBqalja0puvd9ZCr8HlsDx9ZB6HTTFxVMwh14p0kaa7QHr7IF/zptkRYWohBbGX6WKWuOMLhtdXoFF1C/YZHJI60CmBUSyaKwkzzSqrqjwxF2MPGvcHZHXebrIS2kWm0+sssaRKXUAuAprfUZ634l4EvgKWCN1rpAvwIuEmsQr5yGlf+G0J/M2rlOr0LrZ8BdqqHZVVIinNphRmyPrDIf0pOug4ubKZKRkjD6twI3a0G0FFnJTGu4eMRKCFfB0TVw/bJJ9qo2M4nOHV1Mr85FL9teg1ijvUmQTu00o2indkLsaWsnBeVrZkgaGxeuhCIpwayPjdhipotGQJuF3QAAIABJREFUbIXLkeYxN09TVCklGfRvnfVU0Zxcf4k34OR2M2p7bK05X2I8oEyyHdTJJI012oGnA3pIpW1h0fUtaWEhCpUzl+Pp9claKvp4MO/5Dni6F4EK4oWU1pruH67Gt2QJ5jzb3tHhiHy2cv9Znvh+K0Pb1eCdB2R2SU7kdA2ivRLEXVrr4DT3FWZ6aSOlVKjWulm+nzQbhTpBvB5rKvatnwxJN6D106algzNPCyvKEuLgxKbUKamndoBOBveSJoHx9IX9CyAxzTS/4lpkJe6SSQQPr4DDK03yB2aqYEpCGNQp87WcmwT7yunMSWNKUgVmmnBK0lilifm9VHm7vNxciz1njQ5ayeDJ7VaSBpT2t5LBNhDQyoxeu9mxIlvidTOKe2ytGWFM+SJEuZj3LfBO87eq3tb+I7VpW1j0/1ZaWIhCJSlZM3jqZnZERLNgdAdqVSyCMxsKmSmrD/OfxftZ9nIn+XsUAfNCo5i0JJyT0XEoBZVLe7Li1bvki5gccnSC+AVQHfjN2vQQEAm8BizUWnfJ95Nmo1AmiMlJZhrYyvch9gw0eAC6/cuMkgjnEXcJjq23RsVWmxEPW0pVhFFbC9eIVm4lJZhiKSkJ4cntJnn2KG0SjJpdTGJY7g77Vou9et4k7mmTxpTkFExFz5RkMWW00d4Vf5OT4Oze1GQwYjNcOmoec3E3caQkg/6tHd+6ISHO/C2PrTOjjJFbzdRW5WpGfFPWMFZva4pA5QdpYSGKgE+XH+R/fx9gYv/GDGgZ4OhwBHDuynXa/Wc5wzsG8c976zs6HJEH80KjMvW59HBz4b8PNZZWJjnk6ARRYZLCDphFReuAOdpBPTUKVYKoNRxaBkvfhnP7zIfGe94zownC+Y3zxeYauhTelcz6Ob86UKFu6k+fKoWvxYbWcOGQSQYPrzCjTzdizahTtZYmGazZBaq1AFd3x8Z67aKZvpk2abx4OPVxnyqZk8as/iY5GeGMu2RG5FKmi0ZtM+8NmC8Lbo4OtjbndPap4jeumddxbJ35O0dtg+REk9xWa2GK3QTdaV6T+22sAZEWFqII2HL0IgO/3kjvJlX5+JGm0qTbiTzzYwjbT1xi45vdpJpsIdZhwgqiouMyba/m68X6N7o6IKLCx6EJorMpNAniqTBTre/oajPK0n0c1O9T+BKH4iyrPnQl/aD9aDPCeP6AWWN1PSb18RI+4Fc7fdLoV9dMkXR1ospr1y6a0dKUtYQpr7VsUOoIYeCdhWOkNP5yatKYkjieP8DNBL9UhcxJ44lNtovA3PUmeJWzRgi3wPlw85hyhUoNU5PBgNbgW6Pw/zd9PRYiNqWOMJ4MNUWGXEuY9biBHc114N8qc/KbMcFuMgi2/2gq2fb+RFpYiELp0tUb3Dt5LR5uLix84U6pmOlklu09w1M/hjBlSAt6NKzs6HDEbQp6Y5HNr+AVcHTCfQUdTqHk6BHEB4H/AhUxfzcFaK116Xw/WQ44XYKY8QNSu1FmStzOmaaCYOd/QMvh9l1zJOwjp43etTZTh8+Fp0kard+vnErdz8XdTCtOmzT61Ta3/Jral53EGybpOWKNEp7cAWjwKAN3dEpdS1guyP6xFITrsXBmd/qk8dx+q8IqZnRUJ2f9fK+yZopoSjJYtXnxqHYbf9kkzylVUk/tNO+Tq4d5H1KqpF46lrkIEZhkfMg8aWEhCiWtNU//uI3VB84y99kOBPs7oLCTyFZiUjLtJ6wguFoZvh3WytHhiNvU7j/LORUTn2m7jCDmXE4TRHt9xTUR6K213men4xdeGROImAj46x9mpKHDC9Dx5cIx+iJsS0kCbzUFUSnwqWxud3RO/1j8ZTh/0IxCpSSNZ/fC/kWpiQqYNicV6qQmjSkJ5K2KsGQ3RVJrc86UhPDYeki4aq7PgNZmpKxmV7MOzZlGNvOLh7dZV1e9beq2hDjTXuZUKCx6JevnjgoxrSQK++jg7fAsDXXuMTeAuGg4sTG1Suqq/8Cqf5NlGxPXEpIcikLr+w3HWLbvDG/f30CSQyfl5urCQy38mbL6MGcux1OptJNP6xc2VfP1zJQgev0/e3ceHlV5/n/8fWcPgSSQsCWEHVkEWQyg4L7hrlUB0Vq1dau19mtb3Gpbq221tda9Wq2KP60LKuBe3JVFhLBvskkgC1uAJEASsj2/P2aCIUwggZnMTPJ5XVeuZM6c85x7JmeSuedZ7uhIJo7pG6SImq9A9SDOcs6N9nvDhymkehDrG4LYpjP85rumj0fCR+VeT6mIbavqJJBroLJWj0yrlAOTxvZHeVbGXPbWgT2cUfEw9ErPtnVfwK58z/aU3p7ewV6neYYMxgVlAEBoqe/1m5QBty1r+njCRckO2DAb3riynh0M7i1s0pBE/GFZXhGX/Gs2J/ZJ5T9XZ2reYQj7fttuTnv4K24/uy83n9I72OFII72zKI9fvb6Is4/uyNK8YvILS0lLjmfimL5aoKYRgt2DmGVmbwDTgH3r/TvnpgTofOGjKNf39l2bfW8XqREVCx36e75qq672JC11ex2/ex8WvPTDftGtPCuN1hRar1FZCvP+4xke2ePkHxaXSVZR4QOc/gffQ4hP/0PwYgoHrdpB//M9ibTPBLtL08ckcoR2763kllcX0C4hhofGDlZyGOJ6tm/NiO7teDMrl5+f3Eu/rzCSs6OEe6YuI7NbW568YhhRWmgo4AKVICYCJcBZtbY54JAJopmdDTwGRAL/cc49WM9+l+EpozHcORci3YMNkNRFb5DEvyIioG03z1efM/a/b8/2/Xsa5zxVTyMGE9dBhOoIHVRDhxCLb0qwpZlwznHP1KVs3FHCa9cfR7sErRkQDsZmdmHiW0uYl72TET1UTzocVFZV86vXFwLwyPghSg6bSEASROfctYdznJlFAk8BZ+KpmzjPzN51zq2os18b4Fbg2yONtcnpDZI0pYQUSBgF3UZ5bq98t/4PKJQcNswx45QQHi4l2NJMvL0gj2mL8rntjKMY2fMQ874lZJx3TGf+9N4K3piXowQxTDzx+VoWbCzkscuHkNGuVbDDaTH8miCa2e3Oub+b2RP4WInAOXfrIZoYAax1zn3vbe914CJgRZ397sezEM5vjzzqJqY3SBJM+oBCgk0JtoS5tVt38/tpyziuZztuOU1z2cJJq5goLhjcmWkL87n3wgG0iQtyjV45qKzsHTzx+RouGZbORUM0z7Ap+bsHsWbV0sMd8pkO1O7eyAVG1t7BzIYCGc65982s3gTRzG4AbgDo2jXE5lLpDZIEiz6gEBE5bGUVVdzy6gLiYyJ57PKhREZoHlu4GZuZwWtzc3h/ySYmjAix94eyT1FpBb96fRFd2rbivou0ynVT82uC6Jx7z/v9pUPtWw9ff2n39USaWQTwCHBNA2J5FngWPKuYHmY8Is2PPqAQETksf/lgJd9t3sWL1wxXqYQwNTQjmT4dWvPGvBwliCHKOcfvpy1jc3EZb910PK1jm2FZrRAXkJmeZnaUmT1rZh+b2ec1Xw04NBfIqHW7C5Bf63YbYCDwpZllA8cB75rZIZdrFRERETlc/1u2iZfnbOD6E3twar8OwQ5HDpOZMX54BotyClm9ZVewwxEfpi7M493F+dx2Rh+Gdm0b7HBapEAtBfQmsBC4B5hY6+tQ5gF9zKyHmcUAlwPv1tzpnCtyzqU657o757oDc4ALw2oVUxEREQkrOTtKuP2tJQzuksTEMf2CHY4coYuHphMVYUye52PRNgmqDdv38PtpyxjRox0/V73KoAlUgljpnHvaOTfXOTe/5utQBznnKoFbgOl45jNOds4tN7P7zOzCAMUqIiIi4lOFd5l95+CJCcOIidIy++EutXUsZ/TvyNSFeZRXVgc7HPHyvNYWERlhPDJ+iOb4BlGgBvW+Z2Y3A1OBvTUbnXM7DnWgc+5D4MM623wuseicO+XIwhQRERGp3yOfrGbBxkKemDCUrilaZr+5GD88g/8t38zn323h7IGdgx2OAI99uoZFOYU8ecVQ0pPjgx1OixaoBPFq7/faw0od0DNA5xMRERHxqxlrtvH0V+uYMCKDCwanBTsc8aMT+6TSMTGWN+blKEEMAd9+v52nvlzL2GO7cP4xeq0FW0ASROdcj0C0KyIiItIUtu4q47Y3FtG7fWv+cP7RwQ5H/CwqMoLLju3C01+uY3NRGZ2StCptsBSVVHDbG4vonpLAvRfqtRYK/DqQ3sxO836/xNeXP88lIiIiEgjV1Y7fTF7MrrJKnrxiGPExkcEOSQJg7LEZVDt4e0FusENpsZxz3D11KVt37eXR8UNIUEmLkODv38LJwOfABT7uc8AUP59PREREQsS0hXk8NH0V+YWlpCXHM3FMXy4emh7ssBrtma/XMWNNAQ9cMoi+ndoEOxwJkO6pCYzs0Y7JWTn8/OReRGhRlCb35vxcPli6idvP7svgjORghyNefk0QnXN/9H6/1p/tioiISGibtjCPu6YspbSiCoC8wlLumrIUIKySxPkbdvLwx6s575jOXD4849AHSFgbPzyDX09ezNzsHRzXMyXY4bQo6wv2cO+7yzm+Zwo3ntQr2OFILQFbq9nMzjOz283sDzVfgTqXiIiIBNdD01ftSw5rlFZU8dD0VUGKqPGKSiq49bWFpCXH8cAlgzBTj1Jzd87AzrSJjVJNxCZWXukpHxMdGcE/xw9WSYsQE5AE0cyeAcYDvwQMGAt0C8S5REREJPjyC0t9bs8rLOX5metZkltIZVXo1pxzznHH20vYUlzGExOGkRgXHeyQpAnEx0RywZA0Ply2ieKyimCH02I88ulqluQW8bdLB9E5SSUtQk2gZoKOcs4dY2ZLnHN/MrOH0fxDERGRZiuldQwFu8sP2B4ZYdz//goAWsVEMrRrMpnd2jG8ezuGdk0OmUUpXpmzgf8t38zd5/ZjiOZCtSjjMzN49duNvLc4nytHqj8j0GavK+AZb/kYlRgJTYH6q1zm/V5iZmnAdkClL0RERJqh1+duZMeecgzPinQ14qMjeeCSQYzs2Y6s7J1kZe9gXvZOHv98Dc55kscBnRPJ7N6W4d3bkdm9LR3aNH25gRX5xdz/wUpO6due605QyeaW5pguSfTt2IbJ83KUIAbYzj3l/PqNxfRITeD35w8IdjhSj0AliO+ZWTLwELAAz/+L5wJ0LhEREQmCyqpq/vLhSl6clc2JfVI5Z2Annvpinc9VTC8YHL+v2HxxWQULNxZ6E8YdvDZ3Iy/OygagW0orbw9jWzK7t6NX+4SAzgXcs7eSW15bQHJ8NA+PHayVLFsgM2Pc8Azuf38F320upl+nxGCH1Cw557hrylK279nLf64eTauY0Bg9IAfy+2/GzCKAz5xzhcDbZvY+EOecK/L3uURERCQ4ikoquOW1BcxYU8BPR/fg7nP7ERUZwRUN6IFJjIvm5KPac/JR7QHPghXL84vIyt7JvOwdfLFq677adO0SYji2W9t9CePAtCRiovy3hMIf313O+oI9/Pe6kaS0jvVbuxJefjQ0nQc/Wsnkebn84QL1bAXCG/Ny9g3jHpieFOxw5CD8niA656q9cw6P997eC+z193lEREQkONZt2811L2WRu7OEv196DOOOsBxETFQEQ7u2ZWjXtlx/Uk+cc3xfsGffkNSs7B18smILALFREQzJSN43JHVYt7aHvaDM1IW5vDU/l1tP682oXqlH9BgkvLVLiOHMAR2ZujCXO87pS2xUZLBDalbWbdvNn95bwQm9UzWMOwwEqm/3YzO7FJjinHOH3FtERETCwlert3HLqwuIiYzg1euPY3j3dn4/h5nRq31rerVvzfjhXQHYuquM+dk7PQnjhh08/dU6qr5wmEG/Ton7ehiHd2/boFUR1xfs4Z6pyxjRvR23nt7H749Bws+4zAw+XLqZz1Zu5dxBWjzFX/ZWVnHrawuJi47g4XEaxh0OApUg/hpIACrNrAxPqQvnnNOgbhERkTDknOP5mev564cr6dspked+cixd2rZqsvN3aBPHOYM6c473jfuevZUsyilkXvYOsrJ38tb8XP7fNxsASE+Or5UwtqNPh9ZERBjTFubx0PRV5BeWEhVpREUYj14+hKjIgJWFljByYp/2dE6K4415OUoQ/ejhj1ezPL+Y536SScfEpl+EShovIAmic65NINoVERGRpre3sop7pi7jzfm5jDm6I/8cNyTo5SkSYqMY3TuV0b09Q0Mrq6pZuWkX87J3MH/DTmat2860RfkAJMZF0aVtPKu37Kay2jOwqaLKYRhz1+/Yt5COtGyREcZlx3bhyS/W7ltoSY7MzDUFPPv191w5sitnDugY7HCkgSwQI0DN7DPn3OmH2tZUMjMzXVZWVjBOLSIiEta27drLTa/MZ/6Gndx6eh/+7/Q+YTFEzDlHzo5STw/jhh28mZW7LzmsLT05nll3nhaECCUUbdxewkkPfcFvzjyKX2ro8RHZsaecsx/9msT4aN675QTiYzSvM9jMbL5zLvNQ+/n14z8ziwNaAalm1hbP0FKARCDNn+cSERGRwFqeX8T1L2Wxo6ScJ68YyvnHhM+/cjOja0oruqa04tJju/D63Byf++UXljZxZBLKuqa04vieKUyen8MvTu0dFh+GhCLnHLe/tYTCkgomXTtCyWGY8feg+xuB+UA/7/ear3eAp/x8LhEREQmQj5Zu4rKnv8EBb900KqySQ1/qGy6oYYRS1/jhGeTsKGXO99uDHUrY+u+3G/l05RZuP7svA9K0BEm48WuC6Jx7zDnXA/itc66nc66H92uwc+5Jf55LRERE/K+62vHop6v5+X8X0K9zG965ZXSzqFk2cUxf4qP378WIj45k4pi+QYpIQtXZAzvRJi6KyVm+e53l4NZs2cWfP1jBSUe156ejewQ7HDkMAVm2yzn3RCDaFRERkcApKa/kltcW8Oina7hkWDqvXX8cHdo0j1UHLx6azgOXDCI9OR7DM/fwgUsGaYEaOUBcdCQXDUnjo2WbKSqtCHY4YWVvZRW3vr6IhJgo/jH2GA3RDVPBXYJMREREQkJeYSk3/L8sVmwq5u5z+3H9iT0xa15v7i4emq6EUBpkfGZXXpmzkXcX53PVcd2CHU7Y+Pv/VrFyUzEvXJPZbD5caolU+EdERKSFm79hBxc9OZON20t44erh3HBSr2aXHIo0xsD0RPp3TmTyPA0zbaivVm/j+Znrufr4bpzWTyUtwllAEkQz+6wh20RERCS43szKYcKz35IQG8XUX4zi1H4dgh2SSNCZGeMyu7A0r4gV+cXBDifkFezey28mL6ZvxzbcdW7/YIcjR8ivCaKZxZlZO7xlLsysnferOypzISIiEjKqqh1/+WAFE99awvAebXnnF6Pp3aFNsMMSCRkXD0knJjJCi9UcgnOOO95aQnFZBY9NGEJctEpahDuVuRAREWlhissq+OmkeTw3wzMcbNK1I0huFRPssERCStuEGM48uiPTFuWxt7Iq2OGErJfnbOCz77Zy9zn96NdJJS2aA5W5EBERaUHWF+zhR0/NYtbaAv7yo4H86aKBREdqSQIRX8ZnZlBYUsHHy7cEO5SQtGrzLv78wUpO7dueq0d1D3Y44ieB+o+w2czaAJjZPWY2xcyGBehcIiIi0gAz1xRw8VOz2LGnnFeuG8mVI7U6o8jBjO6dSnpyvIaZ+lBWUcWtry0kMS6ah8YO1sJWzUigEsTfO+d2mdkJwBjgJeDpAJ1LREREDsI5x6RZ67n6xbl0Sozj3VtO4LieKcEOSyTkRUYYlx7bhZlrC8jdWRLscELKgx99x6otu/jH2GNIbR0b7HDEjwKVINYM1D4PeNo59w7QoMkNZna2ma0ys7VmdqeP+28ys6VmtsjMZprZAD/GLSIi0qyUV1Zz99Sl3PveCk7t24G3bx5FRrtWwQ5LJGyMPbYLAG/PzwtyJKHji++2Mml2NteO7s4pfbXycXMTqAQxz8z+DYwDPjSz2Iacy8wi8Sxmcw4wAJjgIwF81Tk3yDk3BPg78E//hi4iItI8bN+9lx8//y2vzc3h5lN68exVx9I6NirYYYmElYx2rRjdK5U35+dQXe2CHU7Qbdu1l4lvLaZfpzbccXa/YIcjARCoBHEcMB042zlXCLQDJjbguBHAWufc9865cuB14KLaOzjnahejSQD0ShUREanju83FXPTULBblFPLY5UO4/ex+RERojpDI4Rib2YXcnaXMXrc92KEEVXW147dvLmZXWSVPTBiqkhbNVEASROdcCbAVOMG7qRJY04BD04Has4Bzvdv2Y2a/MLN1eHoQbz2yaEVERJqXj5dv5tJ/zaa8spo3bzyei4Yc8K9URBphzNGdSIqPbvGL1Uyanc1Xq7dxz/kD6NNRdVObq4AkiGb2R+AO4C7vpmjglYYc6mPbAT2EzrmnnHO9vOe4p54YbjCzLDPL2rZtW8MCFxERCWPOOZ76Yi03vDyf3h1a894vT2BwRnKwwxIJe3HRkVw8JI3/Ld9MUUlFsMMJipWbinnwo+84o39Hfjyya7DDkQAK1BDTHwEXAnsAnHP5QEM+ZsgFMmrd7gLkH2T/14GLfd3hnHvWOZfpnMts3759g4IWEREJV2UVVfzq9UU8NH0VFw1J440bj6djYlywwxJpNsZmZlBeWc20RS1vsZqakhZJraL526WDVNKimQtUgljunHN4e//MLKGBx80D+phZDzOLAS4H3q29g5n1qXXzPBo2dFVERKTZ2lxUxrh/f8N7S/K5/ey+PDp+iOYGifjZwPQkjk5LbJHDTP/ywUrWbN3NP8cNJkUlLZq9QCWIk72rmCab2fXAp8BzhzrIOVcJ3IJngZuVwGTn3HIzu8/MLvTudouZLTezRcCvgasD8xBERERC36KcQi58cibrtu7m2asyufmU3vp0XyRAxmVmsDy/mGV5RcEOpcl8umILL8/ZwPUn9uDEPhqV1xKYp6MvAA2bnQmchWde4XTn3CcBOVEDZGZmuqysrGCdXkRExC+mLczjoemryC8sJS05nlP7tWdyVi4d2sTy/NXD6dtJi0aIBFJRSQXD//oplw/P4L6LBgY7nIDbWlzG2Y/NoFNiHFN/MYrYKI1MCGdmNt85l3mo/QJWDMmbEH5iZqlAy14TWESkidRNICaO6cvFQ7WCZXMwbWEed01ZSmlFFQB5haW8MmcjvVITePPno2iXEBPkCEWav6RW0Zx9dCemLczj7nP7N+uh3NXVjt+8uZiS8koenzBUyWEL4tchpmZ2nJl9aWZTzGyomS0DlgFbzOxsf55LRET2V5NA5BWW4vAkEHdNWcq0hS1vQYXm6KHpq/Ylh7WVVlQpORRpQuMyMyguq2T68s3BDiWgXpi1nhlrCvjD+UfTu0PrYIcjTcjfPYhPAncDScDnwDnOuTlm1g94Dfifn88nIiLA7r2V3P/+igMSiNKKKu6eupTvC/aQlhRH5+T4fd9bxwZsEIkEQH5hqc/tm4rKmjgSkZZtVK8UurSN582s3GZXY7T2KBQHDEpPZMKIjEMeJ82Lv98dRDnnPgYws/ucc3MAnHPfacK8iIj/7K2sYuHGQmavLWDm2gIW5xZRVe17TnlJeRVPfL6GulPO28RG0Tk5js5J8aQlx9EpMZ7OyXGkJf3wPT5GQ4qCqaKqmunLNzNpVvaBRYG90pLjmzQmkZYuIsIYe2wGj3y6mpwdJWS0axXskPyi7jB2gDVbd/POonxNVWhh/J0gVtf6ue5HnYFZDUdEpAWoqnasyC9m1roCZq0tYF72DsoqqokwGNQlmZtO7skb83Io2F1+wLHpyfF88dtT2FJcxqaiMjYVlXq+F5aSX1TG5qIylucX+Tw2KT6azklxpCXH7/veKTFuXwLZKSmuUXNwNEeyYXbsKee1uRt5Zc4GNhWV0bVdq31FussqfvhXGx8dycQxfYMYqUjLdFlmFx79bDVvzs/l12ceFexw/MLXMPayimoemr5Kf6dbGH8niIPNrBjPyqXx3p/x3la1XhGRBnLOsb5gD7PWbWf22gJmr9tOUWkFAH06tOby4V0Z1SuFkT1TSIqP9m5vc8CnvzUJRExUBBntWh30k+6yiiq2FJeRX1jG5uJS8gu9yWRhGflFZSzcuJOdJRUHHJeSEEOnpB96In/okfQklB0T44iJivC5yMpdU5YC6M2H18pNxUyalc20RXnsraxmdO8U7r9oIKf260BkhCnBFgkR6cnxnNA7lbeycvjV6X2IjAj/kXL1DWOvb7s0X35NEJ1zGoskInKYthSXMXtdATPXbGf2uoJ9c8vSkuI4c0BHTuidyqheKXRI9P15W02icLgJRFx0JN1SEuiWklDvPqXlVft6IPMLS9lc5EkeNxWVkrOjhG/Xb2dXWeV+x5hBautYCkvKqajafzBJaUVVi/90uqra8cmKLUyavZ453+8gLjqCS4/twjWjunNUx/3LVlw8NL1FP1cioWT88AxueXUhs9YWcNJR4V0fsKyiitjoiP1GKNTQMPaWRysUiIgESVFpBXO+9/QQzlq3nbVbdwOQ3CqaUb1S+EWvVEb3TqV7SqsGFz4PdAIRHxNJz/at6dm+/hXtdu+tZHPRDz2Q+YWeYaxvZOX43D+vsJSluUUMTE9sUQXei0oqeCNrIy/N3kBeYSnpyfHcdU4/xg/PILmVViUVCXVnDuhIcqto3sjKCesEceeecq7/f1mUVVQTHWn7fZCnYewtkxJEEZEmUlZRxfwNO5nlTQiX5hZS7Tz/gIf3aMfYY7swuncqAzonEhHGw5Vax0bRu0MbenfYv/dr5toC8uoZqnTBkzPpnBTHGf07cuaAjhzXM4WYKL9WYgoZa7bs4sXZ2UxdkEdpRRUje7Tj9+f354z+HYmKbJ6PWaQ5io2K5OIh6bz67UZ27imnbRiWm8nZUcLVL84ld2cpT10xjIqqag1jF8zVXdauGcrMzHRZWVncnA/JAAAgAElEQVTBDkNEWpiqasfSvCJPQri2gKwNOymvrCYywhiSkczoXimM7p3KkK7JLaIAsa8V8uKjI/ndef2IjYrk4xVbmLFmG2UV1bSJjeKUfh04c0BHTunbnsS46CBGfuSqqx1frNrKi7Oymbm2gJioCC4eksY1o3owIC0x2OGJyGFakV/MuY/P4I8XDODa0T2CHU6jLMkt5KeT5lFR5XjuJ5mM6NEu2CFJgJnZfOdc5qH2Uw+iiEgjHGyREOcca7fu3tdDOOf7H+bj9evUhquO68bo3imM6JHSImsQHmqO5NjMDErLq5i5toBPVmzms5VbeW9xPtGRxnE9UzhrQEfOGNCRzknhMx+muKyCt7JyeembbDZsL6FTYhwTx/RlwoiuKm4v0gwMSEtkUHoSb8zL4ZpR3cNmmPwX323l5v8uoF1CDK/fMPyAER/SsqkHUUSkgXz1gMVGRXDJsHTKKqqZtbaArbv2ApDRLp7RvVIZ5V1YJrV1bLDCDltV1Y6FG3fyyYotfLxiC+sL9gAwKD2JMwd4hqL269QmJN+Qfb9tNy/Nzuat+bnsKa8is1tbrhndnTFHdyJaw0hFmpWX52zg99OW8d4tJzCoS1Kwwzmk1+Zu5J5py+jfuQ0vXDOcDm1UaKClaGgPohJEEZEGGv3g5/XOoUtJiGFU79R9w0abS+HkUOGcY9223Xy8YgufrNjCwo2FgCcRP7N/J84c0JHh3dsGdQ5fdbXj6zXbmDQ7my9XbSMmMoLzB3fm2lE9wuJNo4gcnqLSCkb85VPGZnbhzxcPCnY49XLO8c9PVvPE52s5+aj2/OvKYSS0wNEsLZmGmIqI+Fl9taAMmPe7M8J6YZlQZ2b7Fr65+ZTebN1Vxmcrt/LJii288u0GXpi1nuRW0ZzW1zNv8aSj2jfZG5/deyuZsiCXSbOz+X7bHtq3ieW2M47iipFdad9GPccizV1SfDTnDOzEO4vyuee8AcRFh96c8vLKau6csoQpC/IYn5nBn380UKMZpF5KEEVEGuCLVVuJMKPKx6iLtOR4JYdNrEObOCaM6MqEEV3Zs7eSr1dv45MVW/jsu61MWZhHTFQEJ/RO5cwBHTm9f4eADKHauL2El77JZvK8HHbtrWRwRjKPXT6EcwZ2brYrsIqIb+OGZzBtUT7/W7Y55Fb9LC6r4OZXFjBzbQG/PvMofnla75Acmi+hQwmiiMhBFOzey/3vr+CdRfl0aBNLYWkF5ZU/FBJWjajgS4iN4pxBnTlnUGcqq6qZl10zb3Ezn3+3FTMYkpHMmQM6ctaATvTuUH8Nx0NxzjF73XZenJXNZ99tIdKMcwd15trR3Rnata0fH5WIhJPjeqSQ0S6eN+blhFSCuLmojGtenMvarbt56LJjGJuZEeyQJAxoDqKIiA/OOd5ekMefP1jBnr2V/OLU3vz8lF58tHSzakSFCecc323exSfeeYtL84oA6JmasG+Rm6Fd2xLZgN7f0vIqpi7MY9Ls9azespuUhBiuHNmVK4/rRsdELfAgIvDEZ2t4+JPVfD3xVLqmBH8e+qrNu7jmxbnsKqvkX1cO46Sj2gc7JAkyLVJTixJEEWmMDdv38Lupy5i5toBju7XlwUsG0aejlgAPd5uKSvnUuyLqnO+3U1HlSG0dw+n9PMniCX1S+d+y/T8A+NkJ3dlSvJfX5+VQVFrB0WmJXDu6B+cf0zkk5xmJSPBsKipl1IOfc8upvfnNWcEdWTJ7XQE3vjyf+OhIXrx2OEenaaEsUYK4HyWIItIQlVXV/Gfmeh79dDVRERHccU4/rhzRVfMLm6Hisgq+WrWNj1ds4cvvtrJrbyXREZ45ptV1/i0acO6gzlwzujuZ3dpq7o6I1OvqF+ayessuZt5xWoNGJwTCO4vy+O2bi+meksCkn44gPTl8asdKYGkVUxGRRliaW8SdU5awPL+YswZ05L6LBtIpSUMHm6vEuGguGJzGBYPTKK+s5tv127np5fnsKa86YN8OibE8deWwIEQpIuFm/PAMbv7vAmas2cYpfTs06bmdczz91Tr+/r9VjOzRjmevyiSpVXSTxiDNgxJEEWnRSsoreeST1Tw/cz0prWN55sfDOHtg52CHJU0oJiqCE/u0p8RHcgiwtXhvE0ckIuHqjP4daZcQw+SsnCZNECurqrn3veW8MmcjFwxO4x9jjyE2SsPg5fAoQRSRFuvr1du4e+pScneWMmFEV+48px9J8fq0taVKS44nz0etyzQNzxKRBoqJiuDiIem8PCebHXvKaZcQE/BzlpRXcutrC/l05VZuPLknd4zpp6kRckRUqElEWpztu/dy2xuL+MkLc4mJiuCNG47jgUsGKTls4SaO6Ut8nYVnVMZERBpr/PAMKqocUxfmBfxcBbv3MuG5b/n8u63cd9HR3HVOfyWHcsTUgygiLYZznn/Y97+/gt17K7n1tN7cfGpvrUYpAPvKlaiMiYgcib6d2jC4SxKT5+Xw09HdA7aw1fqCPVz9wly27irjmR8fy1lHdwrIeaTlUYIoIi1Czo4S7p66lBlrChjWNZkHLz2Go1S6Quq4eGi6EkIROWLjhmfwu6nLWJJbxOCMZL+3v2DjTn42aR5mxqvXH8ewrm39fg5puZQgBsG0hXn6hFqkiVRWVfPCrPX885PVRJpx30VH8+OR3TQER0REAuaCwWnc//4K3sjK8XuCOH35Zm59bSGdkuJ46doRdE9N8Gv7IkoQm9i0hXncNWUppRWe1fLyCku5a8pSACWJDaQEWxpqWZ6ndMWyvGLO6N+B+y4aqAVHREQk4BLjojl3YGfeW5TP788bQHyMf6YyvDQ7m3vfW84xXZJ54epMUlrH+qVdkdqUIDaxh6av2pcc1iitqOKP7y4jNiqCzsnxpCXFkdo6Vj0cPijBloYoLa/ikU89pSvatorhqSuGce6gTipwLiIiTWbc8AymLMzjo2WbuGRYlyNqq7ra8bf/fce/v/6eM/p35IkJQ/2WdIrUpQSxieX7WEIdoKi0kp//d8G+29GRRsfEODonxdE5KZ7OyXGkJcXvdzslIabZvuGtqnbsLCln++5ytu/Zy/bd5ezYU84/6kmwH5q+SgliAzX3HtiZawq4e+pSNu4o4fLhGdx1Tn8VChYRkSY3skc7uqe04o15OUeUIO6trOK3by7hvcX5XHVcN+698Ggi1YkgARRyCaKZnQ08BkQC/3HOPVjn/l8D1wGVwDbgp865DU0e6GGqr85W56Q4nvtJJpuKythUVOr5XlhKflEZC3N28tGyMiqq3H7HxERF0Dkpjk6JcaQle5NHbw9kpyRPQpncKrrRSWQgEojqakdRaQXb95Szffdez3fvzzv2eBLBgpqf95Szs6Qc5w7dbo36Em/ZX3Pugd25p5z7P1jBlAV59ExN4PUbjuO4ninBDktERFooM2NsZgYPTV9FdsGew5orWFRSwfUvZzF3/Q7uPKcfN57Us9l2DkjoCKkE0cwigaeAM4FcYJ6ZveucW1Frt4VApnOuxMx+DvwdGN/00R6eiWP67vcGHTx1tu44ux8D05MYmJ7k87jqasf2PeVsKiolv9CTRG4uKiPfm0jOXb+DzcVlVFXvn1XFRUeQlhRPJ2/PY1ryDz2QNb2RiXFR+/7YNDSBcM5RXFbpTe72UuDt4aud/O3w9vwV7PYkfHVjq5EUH01K6xhSE2Lp1b41I3rEkJIQQ0rrWNolxHju8/584ZMzyS8sO6CNtk1QiLY5qG+I8++nLSM+JpLBXZLplBQXpOgOj3OOdxblc9/7KygureCWU3tzy2kqXSEiIsF36bAuPPzxKt6cn8PEMf0adWzuzhKueXEeG7bv4bHLh3DRkPD+IFfCh7nGdNMEmJkdD9zrnBvjvX0XgHPugXr2Hwo86ZwbfbB2MzMzXVZWlr/DPWyBGuJXVe0o2L2X/EJPD2R+oSeJ3FRURn5RKZsKy9i6q4y6eVpCTCSdvT2Q8zfspKS86oC246Mjyezedt+Qzx17yg/o0azRJi5qvwQvtXWMJ9FLiCWlde3vMbRNiCE6MqLBj7FuAgtgBs7BT47vxt3n9ldiUI89eys5+o/TD7lfhzaxHNMliWO6JO/73i5EE/CcHSXcM20ZX63expCMZB68dBD9OiUGOywREZF9fjppHsvzi5h1x2lENfA9z/L8Iq59cR6lFVX8+6pjGdUrNcBRSktgZvOdc5mH2i+kehCBdCCn1u1cYORB9v8Z8JGvO8zsBuAGgK5du/orPr8IVJ2tyAjPvMWOiXEMrWefyqpqtu7au19PpGc4q+dnX8kheHqZdpVV0jkpjoHpiaS0jvUmgTG0S6j9cwyxUYFL0HwVsr7tjD6s3LyL52euZ+76HTx5xVB6d1B9u9rmb9jBrycvrvf+tKQ4nrhiGEtzC1mSW8Ti3EI++27rvmG+Ge3iOSb9h4RxUJckWscG789HZVU1k2Zn8/DHqzGDP14wgJ8c311zMkREJOSMy+zCTa9sZcaaAk7t1+GQ+3+1ehs3vzKfpPho3rppFH076T2NNK1Q60EcC4xxzl3nvX0VMMI590sf+/4YuAU42Tm392DthloPYigb/eDnPudIpifHM+vO04IQUcN98d1WfvPmYkrLq7j3wgGMy8xo8eP0yyureeyz1Tz95TrSkuO5eEgaz8/MPmCI8wOXDDrgQ4tdZRUsyytmSa2kMXen59owg56pCQyu6WXMSGZA58Qm6b1dkV/MnVOWsCS3iNP6deD+iweSrtIVIiISosorqzn+gc8Y3r0dz1x17EH3nZyVw11TltKnQ2smXTsi7KZ9SGgL1x7EXCCj1u0uQH7dnczsDOB3NCA5lMapb47kxDF9gxhVw5zarwMf/epE/u/1Rdzx9lJmrCngr5cMIjGuZa5guXrLLm57YxHL84sZl9mF358/gDZx0fTu0KZBQ5zbxEVzfK8Uju/1w0Iv23fvZWleEUtyi1iSW8iMtQVMWZgHQFSEcVTHNgzO8PYypifRt1ObRg0hPpiyiioe/XQNz834nratonliwlDOP6Zzi/8QQEREQltMVAQ/GprOpNnZFOzeS6qP2oXOOR77bA2PfrqGE3qn8vSPh9Gmhb5/keALtR7EKGA1cDqQB8wDrnDOLa+1z1DgLeBs59yahrSrHsTGCfcyCFXVjme+Wsc/P1lNWnIcj18+lKFd2wY7rCZTXe14cXY2f/vfd7SJjeKBSwZx1tGdAnIu5xybi8v2JYye70UUlVYAEBsVwYC0RAZ7E8bBGUn0TG3d6Bqfs9Z6Slds2F7CuMwu3H1uf5Jbhea8SBERkbrWbNnFmY98zT3n9ee6E3vud19FVTW/m7qUyVm5XDIsnQcvOYaYKP98uCpSW0N7EEMqQQQws3OBR/GUuXjBOfcXM7sPyHLOvWtmnwKDgE3eQzY65y48WJtKEFum+Rt2cOtri9hSXMZvzurLjSf1bHRiEm7yCkuZ+OZiZq/bzhn9O/DAJcfQvs2Bn1QGknOOjTtKWJxbxJIcT9K4LL9o3/zW1rFRDExP3LcIzuAuyXRpG7/fSro1H1B0Soojo208c7N30j2lFX+9ZJAm6ouISFj60b9msbusko9vO2nf/7zdeyu5+b8L+Hr1Nm49rTe3nXmURsZIwIRtghgIShBbrqKSCu6csoSPlm3mxD6pPDxuMB3aNL/x/M45pi3K4w/vLKeq2vGH8wcwfnjozMGsqnas27abxTmF+3obV27aRXlVNQBtW0UzqEsycVHGl6sK9m2vcUb/Djx5xTCtUCsiImHrtbkbuWvKUqbcPIphXduytbiMayfN47vNu/jLxQO5fERoLaoozY8SxFqUILZszjlenbuR+95bQZu4KP45bggnHdU+2GH5zc495dwzbRkfLN1EZre2PDxuMN1SGl+Mt6mVV1azavMuFucW7hue+t3mXT73DYdFkkRERA5mV1kFw+77hOioCErLq4gwIyICnr0qs0Grm4ocqXBdpEbE78yMK0d2I7NbO3752gJ+8sJcbjypJ785q2/Yj/H/ctVWbn9rCTtLyrn97L7ceFKvsCn1EBMVwaAuSQzqkgR0A6DHnR/g6yOrfB8r64qIiISTz1Zupdqxb8pFlXNEWcS+efsioSK83x2LNELfTm145xcncMXIrvz76+8Z+8xsNm4vCXZYh6WkvJJ7pi3lmhfnkdwqmmm/GM3Np/QOm+SwPmn1lKuob7uIiEi4eGj6KqrqjNzbW1nNQ9NXBSkiEd+UIEqLEh8TyV9/NIh/XTmM7wv2cO7jM3hnUV6ww2qUhRt3ct7jM/nvtxu57oQevHvLCRydlhTssPxi4pi+xNeZZxguZVZEREQOpr7RMBolI6FGQ0ylRTp3UGcGpSfxq9cX8qvXFzFrbQH3Xng0rWJC9yVRUVXNE5+v5akv1tIpMY5XrztuvxqFzUFNOZVwLrMiIiLiS1pyPHk+kkGNkpFQo0VqpEWrqKrm0U9X868v19EjNYEnJgwNyd64tVt38+vJi1iSW8Qlw9K598KjSVQBXRERkbAxbWEed01ZSmlF1b5t8dGRPHDJIH0QKk2ioYvUaIiptGjRkRFMHNOP//5sJLvLKvnRU7N5aXY2ofLBSXW146XZ2Zz3+AxydpTw9JXD+Oe4IUoORUREwszFQ9N54JJBpCfHY3hW6FZyKKFIPYgiXtt37+W3by7mi1XbOHNAR/5+6TG0TYgJWjybi8qY+NZiZqwp4NS+7fnbpcfQIbH51XAUERERkcBTD6JII6W0juX5q4dzz3n9+XLVVs59fAbffr89KLG8uzifsx75iqzsnfzlRwN54ZrhSg5FREREJOCUIIrUEhFhXHdiT6b8fDSxURFMeG4Oj366mqrqpulpLyqp4JevLeTW1xbSq0NrPvzViVw5shtm4V2+QkRERETCgxJEER8GdUni/VtP5OIh6Tz66RomPDcn4MtQz1izjTGPfs1HSzfx27OO4s0bj6dHakJAzykiIiIiUpsSRJF6tI6N4p/jh/Dw2MEsyyvi3Mdn8PHyzX4/T2l5Ffe+u5yrnp9L67gopt48mltO60NUpF6eIiIiItK09A5U5BAuPbYL7//yBNKT47nh5fn88Z1llNVaovpILMkt5PwnZjBpdjbXju7O+788gUFdQq/MhoiIiIi0DKFbFVwkhPRs35opN4/ibx+t4oVZ65mbvZMnJgyld4fWh9VeZVU1//pyHY9/tob2bWJ55WcjOaFPqp+jFhERERFpHPUgijRQbFQkf7hgAC9ck8mW4jIueGImk+flNLpm4vfbdnPZM9/wz09Wc94xnfnfr05ScigiIiIiIUEJokgjndavIx/96kSGZCRz+9tLuPX1RRSXVRzyOOccL8/ZwHmPz2R9wR6evGIoj10+lKRWKnovIiIiIqFBQ0xFDkPHxDheuW4kT3+5lkc+XcOinJ08MWEYQzKSfe6/tbiM299ewpertnFin1QeumwwnZJU11BEREREQos1dnhcOMrMzHRZWVnBDkOaqazsHfzq9UVsKS5j4pi+dGgdyz8+WU1+YSlpyfGcOaAD0xblU1ZRxd3n9ueq41TXUERERESalpnNd85lHnI/JYgiR66opII7pyzho2WbiTCorvOyymgbz6SfjqBX+8Nb1EZERERE5Eg0NEHUHEQRP0hqFc2/rhxGUnz0AckhQJVzSg5FREREJOQpQRTxEzOjuNT3YjWbCsuaOBoRERERkcZTgijiR2nJ8Y3aLiIiIiISSpQgivjRxDF9iY+O3G9bfHQkE8f0DVJEIiIiIiINpzIXIn508dB0AB6avmrfKqYTx/Tdt11EREREJJQpQRTxs4uHpishFBEREZGwpCGmIiIiIiIiAihBFBERERERES8liCIiIiIiIgKEYIJoZmeb2SozW2tmd/q4/yQzW2BmlWZ2WTBiFBERERERaY5CKkE0s0jgKeAcYAAwwcwG1NltI3AN8GrTRiciIiIiItK8hdoqpiOAtc657wHM7HXgImBFzQ7OuWzvfdXBCFBERERERKS5CrUEMR3IqXU7Fxh5pI3Onz+/wMw2HGSXJKCogc01dN+G7JcKFDTwvOGqMc9tIAUyDn+2fSRtHc6x/r72dd17tITr3p/th/t135D9WsJ1D6Fx7eu6P/Jj9F6ncXTdN11bjT1W1/3+ujVoL+dcyHwBY4H/1Lp9FfBEPftOAi47SFs3AFnerxsOcd5nGxFjg/ZtyH5AVrCf8yb4nTb4uQ3XOPzZ9pG0dTjH+vva13Xv/2silOPwV/vhft03ZL+WcN3785oI5Rh03Tduv5Zw7eu6b7q2GnusrvvD+wq1HsRcIKPW7S5A/uE05Jx7Fni2gbu/14imG7pvY9pszkLleQhkHP5s+0jaOpxj/X3th8rvO9hC5XkIdBz+aj/cr/vDjaM5CoXnQdf9kR+j675xQuF5CJfr/kjbauyxuu4Pg3mz3JBgZlHAauB0IA+YB1zhnFvuY99JwPvOubeaNEg/MrMs51xmsOMQaUq67qUl0nUvLZWufWmJwv26D6lVTJ1zlcAtwHRgJTDZObfczO4zswsBzGy4meXiGY76bzM7IHkMIw3t4RRpTnTdS0uk615aKl370hKF9XUfUj2IIiIiIiIiEjwh1YMoIiIiIiIiwaMEUURERERERAAliCIiIiIiIuKlBFFEREREREQAJYghzcwSzGy+mZ0f7FhEmoKZ9TezZ8zsLTP7ebDjEWkKZnaxmT1nZu+Y2VnBjkekKZhZTzN73szCtlyZSEN438+/5P07f2Ww42kIJYgBYGYvmNlWM1tWZ/vZZrbKzNaa2Z0NaOoOYHJgohTxL39c9865lc65m4BxQNjWD5KWw0/X/TTn3PXANcD4AIYr4hd+uu6/d879LLCRigRGI18DlwBvef/OX9jkwR4GJYiBMQk4u/YGM4sEngLOAQYAE8xsgJkNMrP363x1MLMzgBXAlqYOXuQwTeIIr3vvMRcCM4HPmjZ8kcMyCT9c9173eI8TCXWT8N91LxKOJtHA1wDQBcjx7lbVhDEetqhgB9AcOee+NrPudTaPANY6574HMLPXgYuccw8ABwwhNbNTgQQ8F1ipmX3onKsOaOAiR8Af1723nXeBd83sA+DVwEUscuT89PfegAeBj5xzCwIbsciR89ffe5Fw1ZjXAJCLJ0lcRJh0zilBbDrp/PDpAXgulpH17eyc+x2AmV0DFCg5lDDVqOvezE7BMxQjFvgwoJGJBE6jrnvgl8AZQJKZ9XbOPRPI4EQCpLF/71OAvwBDzewubyIpEs7qew08DjxpZucB7wUjsMZSgth0zMc2d6iDnHOT/B+KSJNp1HXvnPsS+DJQwYg0kcZe94/jeQMhEs4ae91vB24KXDgiTc7na8A5twe4tqmDORJh0c3ZTOQCGbVudwHygxSLSFPRdS8tka57aYl03UtL12xeA0oQm848oI+Z9TCzGOBy4N0gxyQSaLrupSXSdS8tka57aemazWtACWIAmNlrwDdAXzPLNbOfOecqgVuA6cBKYLJzbnkw4xTxJ1330hLpupeWSNe9tHTN/TVgzh1yGpyIiIiIiIi0AOpBFBEREREREUAJooiIiIiIiHgpQRQRERERERFACaKIiIiIiIh4KUEUERERERERQAmiiIiIiIiIeClBFBGRgDOzR8zs/2rdnm5m/6l1+2Ez+/Uh2pjdgPNkm1mqj+2nmNmoeo650MzuPES7aWb2lvfnIWZ2biOPv8bMnvT+fJOZ/eRQj+VQj+Fw2wkEb2zvBzsOERE5clHBDkBERFqE2cBY4FEziwBSgcRa948C/s/XgTWccz4TvAY6BdjtjaNuu+8C7x7i3PnAZd6bQ4BM4MOGHl+nrWcaum8dp1DrMRxBOyIiIvVSD6KIiDSFWXiSQICjgWXALjNra2axQH9gIYCZTTSzeWa2xMz+VNOAme32fo8ws3+Z2XIze9/MPjSzy2qd65dmtsDMlppZPzPrDtwE3GZmi8zsxNqB1endm2Rmj5vZbDP7vqZdM+tuZsvMLAa4DxjvbWt8neMvMLNvzWyhmX1qZh3rPhFmdq+Z/dbbK7mo1leVmXXz1Yavx1DTjrfNIWY2x/ucTTWztt7tX5rZ38xsrpmtrvvYvft0NrOvve0uq9nHzM72Po+Lzewz77YR3udmofd7Xx/tJZjZC97f4UIzu6jeq0JEREKOEkQREQk4bw9cpZl1xZMofgN8CxyPpzduiXOu3MzOAvoAI/D01B1rZifVae4SoDswCLjO20ZtBc65YcDTwG+dc9nAM8AjzrkhzrkZhwi3M3ACcD7wYJ3HUQ78AXjD29YbdY6dCRznnBsKvA7cXt9JnHP53jaGAM8BbzvnNvhqowGP4f8BdzjnjgGWAn+sdV+Uc24Enh7aP3KgK4Dp3jgGA4vMrL03pkudc4Px9P4CfAec5I3tD8BffbT3O+Bz59xw4FTgITNLqO95EBGR0KIhpiIi0lRqehFHAf8E0r0/F/HD0M+zvF8Lvbdb40kYv67VzgnAm865amCzmX1R5zxTvN/n40kmG2uat+0VvnoAD6EL8IaZdQZigPWHOsDMRuNJdGt69xrVhpklAcnOua+8m14C3qy1S+3no7uPJuYBL5hZNJ7HvsjMTgG+ds6tB3DO7fDumwS8ZGZ9AAdE+2jvLODCmt5NIA7oCqw82OMQEZHQoB5EERFpKrPxJISD8AwxnYOn928UnuQRwIAHanrWnHO9nXPP12nHDnGevd7vVRzeB6F7a/18qHPV9QTwpHNuEHAjnuSoXt4k8HlgvHNu9+G00QAHfT6cc18DJwF5wMvehW8MTwJY1/3AF865gcAF9cRmeHoea36HXZ1zSg5FRMKEEkQREWkqs/AM29zhnKvy9kol40kSv/HuMx34qZm1BjCzdDPrUKedmcCl3rmIHfEs3nIou4A2fngMh2orCU+iBXD1wRrx9thNxjM0dHUD2vB5Xh9lzBUAACAASURBVOdcEbCz1vzCq4Cv6u53kDi6AVudc8/hSVaH4fl9nGxmPbz7tPMR2zX1NDkdzzxQ8x47tKGxiIhI8ClBFBHxMzPrama7zSzSD21NMrM/+yOuetpvcKx+eFxL8axeOqfOtiLgH2b2Z+fcx8CrwDdmthR4iwOToreBXDy9kP/GM5ex6BDnfg/4ka9Fag7DF8CAmkVq6tx3L/Cmmc0ACg7RTs1w28drLVSTdpA2xuFJnn09hqvxzPVbgmfu5n0+zvd/QIqP7afgmXe4ELgUeAy4HlgOTDGzxUDNXMu/Aw+Y2So8iagv9+MZerrEzJbhGS77Cvj3tVGbd9GeVf5sM1C8CwddF+w4RETqY875GkEiIiKHYmbZQEc8Q/dqHOVdkMVf55gE5Drn7vFx3zXAdc65E/x1vmA52OOsZ//WzrndZpYCzAVGO+c2BzLGYDOzL4FXnHP/8XFfdzxzFaOdc5V+Pu8RtW1m9wK9nXM/9mNMDujjnFvrrzabysF+jyIioUCL1IiIHJkLnHOfBjuI+phZpHOu6tB7hp33zSwZzyIu9zf35FBERKSpaIipiIifmadmnjOzKO/tL83sfjObZWa7zOxjM0uttf+bZrbZzIq89eiObsA5+uMpe3C8d8heoXf7JDN72jy1AfcAp5rZed56dMVmluPt0Wl0rIfxuH5iZhvMbLuZ/d7Mss3sjAY+h9eb2Voz22Fm73qHXmIejwADgB5AJZDlve9cM1vhjSXPflhFs3a7sWZWaGYDa21rb2alZtbBzFLNU1ux0HvuGWZ2wP9KM/uTmT3h/TnazPaY2d+9t+PNrMx+qEV4nHlqBhaap6bgKbXa2Tfc0MwizexhMysws/Vmdkvt59urWz3Pd80qr4Xe66Fu6Y+a+os1Qz1rfpdXm9lG7zl/52tfX22bp/bjzFr7P+a9torNbL7VM4y39jXkbWd3ra8y8/TK19Rb/Mb7nG0ysyfNU4MSM6uJZ7H3uPFmdoqZ5dY6T3/vc1tonnqZF9a6b5KZPWVmH3ifx2/NrFc98caZ2Svea7jQPLUdO3rva2dmL5pZvpntNLNp3u1tvdfQNu/2982si6/2vfv/1MxWevedbp45oSIiQaMEUUSkaVwBXAt0wNPrVTt5+QhPKYcOwALgv4dqzLsq5E3AN8651s655Drn+gueuXszgT3AT/AsCHMe8HMzu/gwY23QvmY2APgXcCWeuoJJeMpaHJKZnQY8gGfOXWdgA556gOApoXAScJT38YwHtnvvex640TnXBhgIfF63befcXjxlHybU2jwO+Mo5txX4DZ75je3xDB++G9+reX7FD4vjDAc2Ayd7bx8PrHLO7TSzdOAD4M9AOzzPz9vmqTNY1/XAOXjmEA4DfP2O6vvd1NSKTPZeD9/4ONaXE4C+wOnAH8zzwUNdDWl7njfudnjmkL5pZgddfdU5V3Pttgba4pmb+pr37irgNjxzVo/3xnez97iaeAZ7j9+vFqV5Fv95D/gYz/P0S+C/Zta31m4TgD95z7sWz+vFl6vxXLsZeOZv3gSUeu97GWgFHO09zyPe7RHAi0A3POU9SoEnfTXufR3ejaccS3tgRq3nQEQkKJQgiogcmWnenoXCmh6EerzonFvtnCvFs3LlkJo7nHMvOOd2eZOXe4HB5qltd7jecc7Ncs5VO+fKnHNfOueWem8vwfMG9OSDHF9vrI3Y9zLgPefczFrF5Rs66f1K4AXn3ALvc3IXnp7S7kAFnsS3H5559Cudc5u8x1XgWTwm0Tm30zm3oJ72X2X/BPEK77aaNjoD3ZxzFc65Gc73ZP1vgD7mmQN5Ep7kNN08q6+ezA+riP4Y+NA596H3+f8ET4/nuT7aHAc85pzLdc7tBB70sU9jfjcN8SfnXKlzbjGwGBh8OI04515xzm13zlU65x4GYvEkng31OJ4PMn7nbW++c26Ot71sPIsRHeyare04PPUzH3TOlTvnPgfeZ//f+RTn3FzvnMr/Uv/zWIEnMeztXXl3vnOu2DzlSc4BbvJeaxU1dSi9z8PbzrkS59wuPMlnfbHfiKesy0pvLH8FhqgXUUSCSQmiiMiRudg5l+z9OlivXO05ciV43sDWDCt80MzWmVkxkO3dJ5XDl1P7hpmNNLMvvEPeivD0ghysfZ+xNnLftNpxOOdK+KGn71DS8PQa1hy723tsuvfN/pPAU8AWM3vWzBK9u16KJ/HaYGZf+Rpm6fU5EO99XrrhSQ6meu97CE+P0sdm9r2Z3emrAW+CloXnjf9JeBLC2cBo9k8QuwFja32IUIin165zPY+79u8ux8c+jfndNIRf2jOz33iHSRZ5H2MSDbyGzexGPL2xVzjnqr3bjvIOzdzsfV38taHt4X0ea9ry2sD+PdgNfdwv4ynb8bp3KOnfvT2UGXjKtez08Xhamdm/zTO8uhjPEN1k871yazfgsVrXxg48dSQb1NsuIhIIShBFRILrCuAi4Aw8b6q7e7c3pEB7fT1ydbe/CrwLZDjnkvDMXWxsAfjG2gTsm3dlZvH4LrHgSz6eN841xyZ4j80DcM497pw7Fs/QvqOAid7t85xzF+EZ7jcNTw/bAbyJw2Q8PUpXAO97e3rw9uT+xjnXE08h+F+b2en1xPkV8P/Zu/P4qKr7/+Ovkz1khRC2EAj7IkGQTcGqoC241rZWbV1rv9XWKlpbrLY/l9pF6laly7dq1WptXb5qXcAFd0RRFtn3fUkQkpCFhKwz5/fHnSSTZAIhmTV5Px+PeczMvXfuPQyBzHvO55wzAxiPU2L5MTATmEzjuL29wL+8vkRIt9YmWWt99Q42ec9wQkhbBXJK8qOe2zPe8Jc4PaDdPeXOpbThZ8zz2t8C3/Ss51jvf4FNODOVpuKUYbb1ZzYfyDZNx44OoHH9xjbz9Az+xlo7GmdJkvNwyrX3Aj2MM1FScz/H6T2d4ml7fUmsr/bvxSmL9v75SLTWfna8bRUR8RcFRBGR0EoBqnF6yLrh9JS01QGgf/3kHce4xiFrbZUxZjJOKAq0l4DzjTFTPe37DW3/gP8f4AfGmHHGmHic9+QLa+0uY8wkT89fLE5JYhXgMsbEGWMuM8akWWtrgTKaLj/i6xqX4JSz1peXYow5zxgz1BhjvM7R2nk+xgkLGzxltB8B/wPstNYWeI551vM+zPT0Fid4JlTxNWnJi8BNxpgsT/D45THfqUYFgBsYfByv8de5U3AmCyoAYowxdwKprRzbwBiTjbO+4pXW2i0+zlkGlBtjRgI/abb/wFHa8wXOz8atxplA6AycsP98K8cfrY3TjTG5nt6/MpySU5enrPkt4G+eSWlijTH1QTAFZ9xhiTGmB3DXUS7xd+B245mYyhiTZoz57vG2U0TEnxQQRURC6xmc8rc8YANNF5E/lg9wFjP/yhhztEXZrwfuMcYcxhkL6LNnzZ+stetxJgd5Hqdn7DBwECcMH+u17wN3AC97XjsEuNSzOxV4HCjGed+KgAc8+64AdnnK+n6MM/6vtWvUh4h+OB/06w0D3gPKccYZ/s1a+1Erp/kMSKSxt3ADTmCtf461di9OD/GvcALUXpweT1+/fx/HmVhlDbASeBMneB1zmRJPCe/vgU895YonH+s1bdWGc7+D8x5uwfk7qcJ3eWxzZwJ9gJdM40ym6z37foHzRcZhnPflhWavvRt42tOei5u1twa4AGeMYCHOZElXWms3teXP20wfnC87yoCNOF8K1M/uegVOYNyE87N9s2f7wzg/F4U4/57fbu3k1tr/An/EKWEtA9Z52i0iEjLG99h7ERER//FM3lKCUzK4M9TtiQTGmLOBv1trNWGJiIgEjXoQRUQkIIwx53sm7EjC6eVbS+MkPNKMcdZPPMc4awRm4ZQm/vdYrxMREfGnsAqInrEZS42zkPB6Y8xvfBxztWcmvlWe2/+Eoq0iInJM38SZMCQfp3Tz0laWjBCHwRmrWYxTYroRpyRYREQkaMKqxNQzKUCStbbcMwHBYuAma+3nXsdcDUy01t4QomaKiIiIiIh0SjGhboA3zzfL5Z6nsZ5b+CRYERERERGRTiysAiI4i0YDK4ChwF89M8019x3PdNJbgJ95Zolrfp5rgWsBkpKSJowcOTKArRYREREROba1eaX0Somnd2pCqJsiXcyKFSsKrbWZxzourEpMvXnWgPovcKO1dp3X9gyg3FpbbYz5MXCxtXbG0c41ceJEu3z58sA2WERERETkGAbdvoAbpw/llm+MCHVTpIsxxqyw1k481nFhNUmNN2ttCc6iw7OabS+y1tavo/U4MCHITRMRERERaZcw7ZsRaRBWAdEYk+npOcQYkwichbMArfcxfb2eXoAzy5uIiIiISGQwJtQtEGlVuI1B7As87RmHGAW8aK2db4y5B1hurX0dmG2MuQCoAw4BV4estSIiIiIiIp1IWAVEa+0aYLyP7Xd6Pb4duD2Y7RIRERERkY6rra1l3759VFVVhbopnVZCQgL9+/cnNja2Xa8Pq4AoIiIiItLZdeUC03379pGSkkJOTg5GpbZ+Z62lqKiIffv2MWjQoHadI6zGIIqIiIiISOdVVVVFRkaGwmGAGGPIyMjoUA+tAqKIiIiISBCE6/JywaZwGFgdfX8VEEVEREREgkj5SMKZAqKIiIiIiHQJu3btYsyYMQE590cffcR5550HwOuvv87cuXMDcp1A0yQ1IiIiIiJBoArT4/fqyjzuf2cz+SWV9EtPZM7MEVw4PivUzTqmCy64gAsuuCDUzWgX9SCKiIiIiASR6dLzmLbdqyvzuP2VteSVVGKBvJJKbn9lLa+uzOvQeevq6rjqqqsYO3YsF110EUeOHOGee+5h0qRJjBkzhmuvvbZhvOi8efMYPXo0Y8eO5dJLLwWgoqKCa665hkmTJjF+/Hhee+21Ftf45z//yQ033ADA1VdfzezZs5k6dSqDBw/mpZdeajju/vvvZ9KkSYwdO5a77rqrQ38uf1EPooiIiIiIBN1v3ljPhvyyVvev3FNCjcvdZFtlrYtbX1rDc0v3+HzN6H6p3HX+CUe97ubNm3niiSeYNm0a11xzDX/729+44YYbuPNOZ+n1K664gvnz53P++eczd+5cdu7cSXx8PCUlJQD8/ve/Z8aMGTz55JOUlJQwefJkzjrrrKNec//+/SxevJhNmzZxwQUXcNFFF7Fw4UK2bt3K0qVLsdZywQUXsGjRIk477bSjnivQ1IMoIiIiIhIEqjA9Ps3D4bG2t1V2djbTpk0D4PLLL2fx4sV8+OGHTJkyhdzcXD744APWr18PwNixY7nssst49tlniYlx+tYWLlzI3LlzGTduHGeccQZVVVXs2eM7sNa78MILiYqKYvTo0Rw4cKDhPAsXLmT8+PGcdNJJbNq0ia1bt3boz+YP6kEUEREREQkizWLqOFZP37S5H5BXUtlie1Z6Ii9cd0q7r9t8GQhjDNdffz3Lly8nOzubu+++u2EdwQULFrBo0SJef/11fvvb37J+/Xqstbz88suMGDGiyXnqg58v8fHxDY/ry1ettdx+++1cd9117f6zBIJ6EEVEREREJOzMmTmCxNjoJtsSY6OZM3NEK69omz179rBkyRIAnnvuOU499VQAevbsSXl5ecMYQbfbzd69e5k+fTr33XcfJSUllJeXM3PmTP785z83BL2VK1e2qx0zZ87kySefpLy8HIC8vDwOHjzYoT+bP6gHUUREREQkCKymMT0u9bOV+nsW01GjRvH0009z3XXXMWzYMH7yk59QXFxMbm4uOTk5TJo0CQCXy8Xll19OaWkp1lp+9rOfkZ6ezh133MHNN9/M2LFjsdaSk5PD/Pnzj7sd3/jGN9i4cSOnnOL0hiYnJ/Pss8/Sq1evDv35Osp0hR/UiRMn2uXLl4e6GSIiIiLShdW53Az99Vv8/OvDufHMYaFuTkhs3LiRUaNGhboZnZ6v99kYs8JaO/FYr1WJqYiIiIiIiAAKiCIiIiIiQdH56/akM1BAFBEREREJIs1iKuFMAVFEREREREQABUQRERERkaDoAnNDSieggCgiIiIiEkTNF2oXCScKiCIiIiIi0iXs2rWLMWPGtPn4f/7zn+Tn5x/zmBtuuKGjTQsbCogiIiIiIkFgNY/p8VvzIvxpDNyd7tyveTGol29LQAyUurq6kFw3JiRXFREREREROZo1L8Ibs6G20nleutd5DjD24naftq6ujquuuoqVK1cyfPhwnnnmGR544AHeeOMNKisrmTp1Ko8++igvv/wyy5cv57LLLiMxMZElS5awbt06brrpJioqKoiPj+f9998HID8/n1mzZrF9+3a+9a1vcd999wGQnJzMTTfdxPz580lMTOS1116jd+/e7N69m2uuuYaCggIyMzN56qmnGDBgAFdffTU9evRg5cqVnHTSSaSkpLBz507279/Pli1beOihh/j888956623yMrK4o033iA2NrZDb3Nz6kEUEREREZHge+s2eOrc1m+v3dAYDuvVVjrbW3vNW7cd87KbN2/m2muvZc2aNaSmpvK3v/2NG264gWXLlrFu3ToqKyuZP38+F110ERMnTuTf//43q1atIjo6mksuuYRHHnmE1atX895775GYmAjAqlWreOGFF1i7di0vvPACe/fuBaCiooKTTz6Z1atXc9ppp/H4448DcMMNN3DllVeyZs0aLrvsMmbPnt3Qvi1btvDee+/x4IMPArB9+3YWLFjAa6+9xuWXX8706dNZu3YtiYmJLFiwwB9/E00oIIqIiIiIBIFmMT1Orurj295G2dnZTJs2DYDLL7+cxYsX8+GHHzJlyhRyc3P54IMPWL9+fYvXbd68mb59+zJp0iQAUlNTiYlxCjLPPPNM0tLSSEhIYPTo0ezevRuAuLg4zjvvPAAmTJjArl27AFiyZAnf//73AbjiiitYvHhxw3W++93vEh0d3fD87LPPJjY2ltzcXFwuF7NmzQIgNze34Xz+pBJTEREREZEg0iSmHmfPPfr+P41xykqbS8uGH7S/56z5LLLGGK6//nqWL19OdnY2d999N1VVVS1eZ61tdQba+Pj4hsfR0dEN4wdjY2MbXuO9/WhtSkpK8nnuqKioJueLiooKyDhF9SCKiIiIiEj4OfNOiE1sui020dneAXv27GHJkiUAPPfcc5x66qkA9OzZk/Lycl566aWGY1NSUjh8+DAAI0eOJD8/n2XLlgFw+PDhdge0qVOn8vzzzwPw73//u6EN4UA9iCIiIiIiEn7qJ6J5/x4o3Qdp/Z1w2IEJagBGjRrF008/zXXXXcewYcP4yU9+QnFxMbm5ueTk5DSUkAJcffXV/PjHP26YpOaFF17gxhtvpLKyksTERN577712tWHevHlcc8013H///Q2T1IQLY7tAMfTEiRPt8uXLQ90MEREREenCqmpdjLzjbX45ayQ/OWNIqJsTEhs3bmTUqFGhbkan5+t9NsassNZOPNZrVWIqIiIiIhIEXaBfRjoBBUQRERERkSDSJDUSzhQQRUREREQkaLrCELdQ6uj7q4AoIiIiIhIEFgWjhIQEioqKFBIDxFpLUVERCQkJ7T6HZjEVEREREQmirlxh2r9/f/bt20dBQUGom9JpJSQk0L9//3a/XgFRRERERESCIjY2lkGDBoW6GXIUKjEVEREREQkCVVVKJFBAFBEREREJIs1iKuFMAVFEREREREQABUQRERERkaBQhalEAgVEEREREZEgMl16HlMJd2EVEI0xCcaYpcaY1caY9caY3/g4Jt4Y84IxZpsx5gtjTE7wWyoiIiIiItL5hFVABKqBGdbaE4FxwCxjzMnNjvkhUGytHQr8CfhjkNsoIiIiInLctDi8RIKwCojWUe55Guu5Nf+X9E3gac/jl4AzjdFcUCIiIiISGfTJVcJZWAVEAGNMtDFmFXAQeNda+0WzQ7KAvQDW2jqgFMjwcZ5rjTHLjTHLCwoKAt1sERERERGRiBd2AdFa67LWjgP6A5ONMWOaHeLrO5cW/fXW2sestROttRMzMzMD0VQRERERkTZTgalEgrALiPWstSXAR8CsZrv2AdkAxpgYIA04FNTGiYiIiIiIdEJhFRCNMZnGmHTP40TgLGBTs8NeB67yPL4I+MBqxK+IiIiIiEiHxYS6Ac30BZ42xkTjhNcXrbXzjTH3AMutta8DTwD/MsZsw+k5vDR0zRURERERaRt1aUgkCKuAaK1dA4z3sf1Or8dVwHeD2S4REREREX/RBPwSzsKqxFRERERERERCRwFRRERERCQYVGIqEUABUUREREQkiFRgKuFMAVFEREREREQABUQRERERkaCwqjGVCKCAKCIiIiISRJrEVMKZAqKIiIiIiIgACogiIiIiIkFhVWEqEUABUUREREQkiFRhKuFMAVFEREREREQABUQRERERkaBQhalEAgVEEREREZEgMprGVMKYAqKIiIiISBBYzVIjEUABUURERERERAAFRBERERGRoFKFqYQzBUQRERERkSBQgalEAgVEEREREZEgUgeihDMFRBEREREREQEUEEVEREREgkKTmEokUEAUEREREQkmzVIjYUwBUURERERERAAFRBERERGRoLCax1QigAKiiIiIiEgQqcBUwlnAA6IxJsoYkxro64iIiIiIiEjHBCQgGmP+Y4xJNcYkARuAzcaYOYG4loiIiIhIRFCFqUSAQPUgjrbWlgEXAm8CA4ArAnQtEREREZGIoUlMJZwFKiDGGmNicQLia9baWvSdiYiIiIiISFgLVEB8FNgFJAGLjDEDgbIAXUtEREREJOypt0QiQUwgTmqtnQfM89q02xgzPRDXEhERERGJJEbzmEoYC9QkNTd5JqkxxpgnjDFfAjMCcS0RERERERHxj0CVmF7jmaTmG0Am8ANgboCuJSIiIiIS9qxqTCUCBCog1vebnwM8Za1djdYEFRERERHRLKYS1gIVEFcYYxbiBMR3jDEpgDtA1xIRERERERE/CMgkNcAPgXHADmvtEWNMBk6ZqYiIiIhIl2Q1j6lEgEDNYuo2xvQHvm+cPvSPrbVvBOJaIiIiIiKRRBWmEs4CNYvpXOAmYIPnNtsYc28griUiIiIiIiL+EagxiOcAX7fWPmmtfRKYBZwboGuJiIiIiIS9t9d9BcBtr6xl2twPeHVlXohbJNJSoAIiQLrX47QAXkdEREREJKy9ujKPuW9tanieV1LJ7a+sVUiUsBOogHgvsNIY809jzNPACuAPAbqWiIiIiEhYu/+dzVTXNZ3Uv7LWxf3vbA5Ri0R8C0hAtNY+B5wMvOK5nWKtff5YrzPGZBtjPjTGbDTGrDfG3OTjmDOMMaXGmFWe253+/xOIiIiIiPhPfknlcW0XCRW/zmJqjDmp2aZ9nvt+xph+1tovj3GKOuDn1tovPWsnrjDGvGut3dDsuE+stef5o80iIiIiIoHWNz2B/JKqFtv7pSeGoDUirfP3MhcPHmWfBWYc7cXW2v3Afs/jw8aYjUAWzkyoIiIiIiIR6WvDevLCsn1NtiXGRjNn5ogQtUjEN78GRGvtdH+dyxiTA4wHvvCx+xRjzGogH/iFtXa9j9dfC1wLMGDAAH81S0RERETkuFTVuvh4cyEDeyRS57bkl1TRLz2ROTNHcOH4rFA3T6QJf/cg+oUxJhl4GbjZWlvWbPeXwEBrbbkx5hzgVWBY83NYax8DHgOYOHGiDXCTRURERER8embJLr4qq+L5a0/m5MEZoW6OyFEFcpmLdjHGxOKEw39ba19pvt9aW2atLfc8fhOINcb0DHIzRURERESOqbSylr9+uJ3Th2cqHEpECKuAaIwxwBPARmvtQ60c08dzHMaYyTh/hqLgtVJEREREpG0eW7Sd0spabp2lsYYSGQJSYupjNlOAUmC3tbbuKC+dBlwBrDXGrPJs+xUwAMBa+3fgIuAnxpg6oBK41FqrElIRERERCSsHy6p4YvFOLjixHyf0Swt1c0TaJFBjEP8GnASsAQwwxvM4wxjzY2vtQl8vstYu9hzfKmvtX4C/+Le5IiIiIiL+Ne+DrdS5LD//xvBQN0WkzQJVYroLGG+tnWitnYAzG+k64CzgvgBdU0REREQkLOwqrOD5pXv53uQBDMxICnVzRNosUAFxpPfSE56F7sdba3cE6HoiIiIiImHjwXe3EBsdxY1nDg11U0SOS6BKTDcbY/4XeN7z/BJgizEmHqgN0DVFREREREJuXV4pb6zO54bpQ+mVkhDq5ogcl0D1IF4NbANuBn4G7PBsqwWmB+iaIiIiIiIhd987m0nvFsu1pw8OdVNEjltAehCttZXAg55bc+WBuKaIiIiISKh9tr2QRVsK+PU5o0hNiA11c0SOW6CWuZgG3A0M9L6GtVZfo4iIiIhIp2St5Y9vb6ZvWgJXnDIw1M0RaZdAjUF8Aqe0dAXgCtA1RERERETCxjvrv2L13hLu+85YEmKjQ90ckXYJVEAstda+FaBzi4iIiIiElTqXm/vf2czQXsl8+6SsUDdHpN0CFRA/NMbcD7wCVNdvtNZ+GaDriYiIiIiEzMtf7mN7QQV/v3wCMdGBmgdSJPACFRCneO4nem2zwIwAXU9EREREJCSqal08/N5WxmWnM/OE3qFujkiHBGoWUy1lISIiIiJdwjNLdrG/tIqHLh6HMSbUzRHpEL8GRGPM5dbaZ40xt/jab619yJ/XEwlHr67M4/53NpNfUkm/9ETmzBzBheM1FkFERKQzKq2s5a8fbuf04ZmcMiQj1M0R6TB/9yAmee5T/HxekYjw6so8bn9lLZW1zuS9eSWV3P7KWgCFRBERkU7osUXbKa2sZc7MEaFuiohf+DUgWmsf9dz/xp/nFYkELrfl929ubAiH9SprXdz/ziYFRBERkU7mYFkVTy7exQUn9mNMVlqomyPiFwEZg2iMyQR+BOR4X8Nae00gricSCtZadhcdYfG2Qj7dVshn24sosrK0QQAAIABJREFUraz1eWxeSRW3v7KW6SMymTq0J8nxgZofSkRERIJl3gdbqXW5ueXrw0PdFBG/CdSn1NeAT4D3ANcxjhWJGEXl1Xy2vYhPtxWyeFsh+4orAeiXlsA3RvfmvY0HKD7SMiQmxETx+qo8nlu6h9how6ScHpwxIpMzRvRiWK9kDWgXERGJMLsKK3h+6V6+N3kAOT2Tjv0CkQgRqIDYzVr7ywCdWyRoKmtcLNt1iE+3FfLJ1kI27C8DICUhhqlDMrjutMFMG9qTQT2TMMa0GIMIkBgbzb3fzuWc3L4s332IjzcX8NHmAv7w5ib+8OYm+qUlcPqIXpwxIpNp6l0UERGJCA++u4XY6ChuPHNoqJsi4lfGWuv/kxrzO+Aza+2bfj95O0ycONEuX7481M2QCOByW9bllbJ4WyGLtxayYncxNS43sdGGCQO7c+rQnkwb2pPcrLRWF8Ft6yym+SWVfLylgI82H+TTbUWUV9epd1FERCQCrMsr5bw/L+aG6UP5hSankQhhjFlhrZ14zOMCFBAP48xoWg3UAgaw1tpUv1+sDRQQpTXWWnbVjyPcWshn2wspq6oDYFTfVE4dmsG0oT2ZPKgH3eIC17NXU+du0ru4+cBhAPUuioiIhKErn1zKmn0lLLp1OqkJsaFujkibtDUgBuTTprVWy1xI2Coqr+bT7UV8utUZR5hX0jiOcNaYPpw6LJOpQzLomRwftDbFxUQxdUhPpg7pye3njGrSu/jG6vwuNXZR60iKiEg4+2x7IYu2FPDrc0YpHEqn5NceRGPMSGvtJmPMSb72W2u/9NvFjoN6ELu2yhoXSz3jCBd7jSNMTYhh6pCeTBvWk1OH9iQno1tYBq6u1Lt4tDGcCokiIhJq1lou/NtnHCyr4sNfnEFCbHSomyTSZiEpMTXGPGatvdYY86GP3dZaO8NvFzsOCohdi8ttWZtX6plYpoAvd5dQ43ITFx3ljCP0BMIxWWlER4VfIDyW1sYuThzo9C5OHxm+vYvWWsqr6ygqr6GooprC8hqKymsoLK+mqLyaF5fvpbLW3eJ1WemJfHpbSP77EBERafD2uq/48bMruO87Y7l4UnaomyNyXEI6BjHcKCB2Ls1LEH/xjeGMG9CdxVsLWLytkCXbixrGEY7um8rXhjkTy0zK6UFiXOf6pq89vYv+LuGsdbkprqhxwl5FtSfseZ6Xe55XNAbB6rqWARCcHt36v7fmDLBz7rntbqOIiEhH1bnczHx4EQDv3Hxaq5PViYSrkAdEY8wYYDSQUL/NWvtMQC52DOEWEMN9jFU4ts/ttlTVuXjly338dv7GJiHDAPU/xVnpiQ2BcOqQDDKCOI4wHByrd9FtLfPe39qkl655CWd9L19jwHOCX2NPX01D6Cssr6bEx7qPALHRhp7J8WQkx5GR5NxnNnveMzmensnxdE+KJT4mmmlzP2gYE+qtW1w0y//fWQGdKEhERORoXly2l1tfXsPfL5/ArDF9Qt0ckeMW6llM7wLOwAmIbwJnA4uttRf5/WJtEE4BMdzHWB1v+6y1VNe5qaxxUVnrudW4qKp1UVXrbthW5WN//ePK2ubP3Z7XN25rrdepXlpiLK/9dBoDw3QcYSjU1LlZsbuYj7Yc5KNNjb2LvsTHRDG8d4oTCCtqqGnl/U5LjKVnchwZyfHOfZOg52zPSHLuUxNijvvvwtfPX0yUoc5tGZyZxLxLxzMmK+24zikiItJRVbUupj/wEb1TE/jv9VP1WUMiUqgD4lrgRGCltfZEY0xv4B/W2vP9frE2CKeA2FoPSXJ8DN+fMoD6v4/6v5b6vx1rwdJ0n/PYHvM4730039fs+AVr93OkpvHDeb246CiG90n2hDt3k3DXHgmxUSTGRpMQG914H9f8secYz/bE2GjufWuTz/OpBPHY8ksqmTr3g1b3Tx+R6QS85Dh6egW/+vvu3eKIiwl8OY2vHuxeqfH87IVVFFfUcuusEVwzbRBRETh+VEREItPji3bw+zc38tyPTuaUIRmhbo5Iu4R0mQug0lrrNsbUGWNSgYPA4ABdK6Lk+wiHAOXVdTyzZBcGQ/2XUvUff+u/pTJeG733+Tq+8XH9FUzD8+b7vK/pKxwC1Ljc9E5JaBLWEuOcMFcf9ry3JfoMfM4tPiaq3R/un1my22fA7pee2K7zdSX90hPJSk/0+f5lpSfy1A8mh6BVLV04Pstnb/XbN53GrS+v4XcLNvLJ1kIe+O6JZKZ0rRJiEREJvrKqWv760TZOG56pcChdQqAC4nJjTDrwOLACKAeWBuhaEaXfUT6gh8Msja31cGalJ/LE1ZNC0KKm5swc4bMEds7MESFsVeSI5Peve1Icj10xgX9/sYffzt/A2Y8s4oHvnsgZI3qFumkiItKJPfbxDkqO1HJrBPyuFPEHv9eLGae7615rbYm19u/A14GrrLU/8Pe1ItGcmSNIbLZmTjh9QA/39l04Pot7v51LVnoiBie4hsv4zUgQ6e+fMYbLTx7IGzeeSs/keK5+ahm/nb+B6rr2lTqLiIgczcGyKp5YvJPzT+ynMfDSZQRqDOIKa+0Ev5+4ncJpDCKE5yyh3sK9fSLgTBhw75sbeXrJbk7ol8q8741nSGZyqJslIiKdyB2vruO5pXt475bTyemZFOrmiHRIqCep+SvwT2vtMr+fvB3CLSCKiP+8t+EAc15aTVWtm7svGM3FE7M1u5yIiHTYrsIKznroYy6dnM3vLswNdXNEOqytATFQUxJOB5YYY7YbY9YYY9YaY9YE6Foi0oWdNbo3b998GicNTOeXL6/lhv+spLSVtRlFRETa6qF3txAbHcXsGcNC3RSRoArUJDVnB+i8IiIt9E5N4F/XTOHRRTt4cOFmVu0t4eFLxzEpp0eomyYRRiX2IgKwLq+U11fn89PpQ+iVmhDq5ogEVaB6EH9nrd3tfQN+F6BriYgQFWX4yRlDePknU4mJNlzy6BL+9O4W6lzuUDdNIsSrK/O4/ZU15JVUYoG8kkpuf2Utr67MC3XTRCTI7n9nM+ndYrnu9CGhbopI0AWqB/EE7yfGmGggbCatEZHO68TsdBbM/hp3vraOR97fyqfbCnn40nH0794t1E2TMFJ6pJYdheXsKqpgZ0EFOworeGf9V9S6mo7Lr6x18ce3N6kXUaQLWbK9iI+3FPCrc0aSmhAb6uaIBJ1fA6Ix5nbgV0CiMaasfjNQAzzmz2uJiLQmOT6Ghy4ex+nDM/n1f9dx9iOfMPfbYzl3bN9QN02C6EhNHbsKj7CzsIJdRRXsKKhgZ2E5u4qOcKiipuG4KANZ3RNbhMN6+0uruO5fy7loQjZnjMgkNjpQxTciEmrWWv749ib6piVw5Sk5oW6OSEgEahbTe621t/v9xO2kWUxFuq49RUeY/fxKVu0t4ZKJ2dx1wWi6xQWqeEKCrabOzd7iI+wsqGBnYQU7PT2COwsr+KqsqsmxvVPjyclIYnBmEoN6JjU8zu7RjfiYaKbN/YC8ksoW10iOjyYhNprC8hoykuK4cHwWF03oz6i+qcH6Y4pIkLy97it+/OwK/vidXC6ZNCDUzRHxq5AucxFuFBBFurZal5tH3tvKXz/axqCMJOZ9b7wWPA6R9kwC43Jb8ksqnXLQQqcnsP7xvuJKXO7G32Pp3WIZ1DOJQRlOCByU6QTBnJ5JJMcf/YsBZwziWiprXQ3bEmOjuffbuZw7ti+LthTw0op9vLfxALUuywn9UrloQn++OS6LHklxHXtjRCTk6lxuZj3yCdZa3rn5NGJULSCdjAKiFwVEEQH4bHsht7ywmqKKan45ayTXTBtEVJTWTAyWowWwb47rR0F5NTs94W9HYUXD411FR6ipa5xsqFtcNDkZTvgb7OkJHJTphMLuHQxqbQmwxRU1vL46n5dW7GNtXimx0YYZI3upBFUkwr24bC+3vryGv19+ErPGaEiCdD4RGRCNMdnAM0AfwA08Zq19pNkxBngEOAc4Alxtrf3yaOdVQBSResUVNfzy5TUs3HCA04dn8sB3TyQzJT7UzfKbUC3TYK2l1mWpcbmpqfO6uVxU17mpdVn+5+llFJbXtHhtbLQhPiaa8uq6JtsGZiT5LAntlRKP86sg9DZ9VcbLK/bx35X5FJZXk5EUxzfHOSWoo/upBFUkUlTVupj+wEf0Sk3g1eunhs3/MSL+FJKAaIw56qJj1tpDx3h9X6CvtfZLY0wKsAK40Fq7weuYc4AbcQLiFOARa+2Uo51XAVFEvFlr+fcXe/jt/A2kJMTwwHdP5IwRvULdrA7z1UMXFx3FZSdnMy67uyewNYa3Ws/j6hahrunj6rpmr2l2fP3rO+KqUwZ6SkKTGZSRRL/0hIgq76p1uVuUoI7uW1+C2o+M5M7zJYRIZ/T4oh38/s2N/OdHU5g6pGeomyMSEKEKiDsBizNzaXPWWjv4OM/3GvAXa+27XtseBT6y1j7neb4ZOMNau7+18yggiogvWw4cZvZzK9n01WGumTaIX549gviY6FA367i43ZbtBeUs21XMb+dvaBIO2youJor46CjiYrxu0T4e+9rmuXm/PrbZ8fGe7XP+bw1FFS17ELPSE/n0thn+eDvCQnFFDW+scUpQ1+wrJSaqvgS1P9NH9lIJqkiYKauq5bT7PmRs/3SeuWZyqJsjEjBtDYh+ncrPWjvIX+cyxuQA44Evmu3KAvZ6Pd/n2dYkIBpjrgWuBRgwQLNQiUhLw3un8OpPp3Hvmxt58tOdfL6jiHnfG8/QXsmhblqrqmpdrNlXyrJdh1ixu5gVu4spraw96msM8O4tpzcENe9wFxNlglZKdcd5o32OQZwzc0RQrh8s3ZPiuPKUHK48JYfNXx3m5S/38cqXeSzccEAlqCJh6LGPd1BypJZbO9n/RSLtFbAxiMaY7sAwIKF+m7V2URtfmwx8DPzeWvtKs30LgHuttYs9z98HbrXWrmjtfOpBFJFjeW/DAea8tJqqWjd3nT+aSyZlh8UYlKLyapbvLmb5rkMs313MurzShvX6BmcmMWlgDybkdGfiwO5c8cQX5JVUtThHOPXQhWqMZKjVudws2uopQd1wkBqXWyWoImHg4OEqTr/vI84a3Zs/f298qJsjElAhnaTGGPM/wE1Af2AVcDKwxFp7zE8oxphYYD7wjrX2IR/7VWIqIgFxoKyKW15cxafbijgntw/3fmssad1ig3Z9ay3bCypYsfsQy3cVs3x3MTsLKwBnLGFu/zQm5nRn4sAeTBjYvcXSCkebJbQrhLBI4asEdXp9CeqIXsTFqARVJFjueHUdzy3dw3u3nE5Oz6RQN0ckoEIdENcCk4DPrbXjjDEjgd9Yay85xusM8DRwyFp7cyvHnAvcQOMkNfOstUctGFdAFJG2crstj32ygwfe2UyvlHge+d54JuUcdf6tdquqdbEur7Shh3DF7mKKjzjlot27xTJhYHcm5vRg4sDujMlKIyH22OMju2oPXaTyLkEtLK+mR1Ic3xzXj4sm9OeEflqrUySQdhdVcOaDH3Pp5Gx+d2FuqJsjEnChDojLrLWTjDGrgCnW2mpjzCpr7bhjvO5U4BNgLc4yFwC/AgYAWGv/7gmRfwFm4Sxz8QNr7VHTnwKiiByv1XtLuOn5lew5dIQbZwzjxhlDOzyr5qGKGlbsLma5p4dw7b5SalzOf3WDeiYxYWB3JuV0Z8LAHgzJTAqLElcJjjqXm0+2FvLSin28u+EANS43o7xKUHuqBFXE72Y/t5J3Nxzg4zln0Cs14dgvEIlwoQ6I/wV+ANwMzACKgVhr7Tl+v1gbKCCKSHuUV9dx12vrefnLfUwc2J2HLx1H/+7d2vRaay07CytYvruYFbuKWbb7EDsKnHLR2GjDmKw0JuU4paITBnZXAJAGJUdqeGO1U4K6ulkJ6uHKWv703lb1EIt00Pr8Us6dt5ifTh/CnJkjQ90ckaAIaUBs1pDTgTTgbWtty/nNg0ABUUQ64rVVefy//64DA98en8V7Gw+2+IBeXediXV5Zw/jBFbuLG5Z0SEusLxd1xg+O7d+2clGRLQcO8/KKfbyyMo+Cw9Ut9muMqUj7XPXkUlbtLWHRrdNJSwzeWHORUAp5QDTGRAO98VpKw1q7JyAXOwYFRBHpqL2HjnD5Pz5n96HKJttjogwDenRjX0llw2LxAzO6ecpFnfGDQzKTiYpSuai0X53LzZQ/vO9zHcmUhBgevWIC47LT6Rbn19WrRDqlJduL+N7jn/Orc0Zy7WlDQt0ckaAJyTqIXhe/EbgLOEDjWEILjA3E9UREAi27Rzdq3S2/UKtzW/YWH+HKU3KYlNOdkwZ2p1eKxrKIf8VER3HIRzgEOFxVx/cf/4KYKMMJ/VKZMLCHM5Y1Rz+LIs1Za/nj25vom5bAlafkhLo5ImEpUF813gSMsNYWBej8IiJBt9/HGoMAdS7LHeeNDnJrpKvpl55IXklli+190xL4w7dyGyY/+vcXu3ny051AY2/2RE9oVG+2dHULNxxg1d4S/vidXJX6i7QiUAFxL1AaoHOLiIREax/Q+6UnhqA10tXMmTnC5zqXv5w1kukjezF9ZC8AaurcrM8v9ayleYiPNxfwypd5gDMeduJAp3dxUk4Pctu4fEpnoWVgurY6l5v739nMkMwkvnNS/1A3RyRsBSog7gA+MsYsABpG1fta+F5EJFK09gF9zswRIWyVdBX1QeZYAScuJorxA7ozfkB3fsRgrLXsKjrCsl2HWOEJje9vOugcGx1Fbv80JzR61t3skRQX9D9bMLy6Mq/Jv9+8kkpuf2UtgEJiF/HKyjy2HSzn75ef1OFli0Q6s0Atc3GXr+3W2t/4/WJtoElqRMRf1AMhnUHDmpy7DrF8d9M1OQdnJjFpYI+GXsacjG4Rtyany20pqqjmYFk1BYed2+8WbKCsqq7FsVnpiXx624wQtFKCqarWxYwHPiIzNYFXr58acT/TEiHWvAjv3wOl+yCtP5x5J4y9ONStahDyWUzDiQKiiIhI66pqXazN85Sl7jrEij3FlBypBSAjKa5hVt4JOd0Z0y+NuJjQ9L5U1bqc0FdexcGyag56wt/Bw1Vej6spKq/Gx5xSPhlg59xzA9puCb1/fLKD3y3YyH9+NIWpQ3qGujnSGa15Ed6YDbVeQ1FiE+H8eWETEkMyi6kx5mFr7c3GmDdwZi1twlp7gT+vJyIiIh2XEBvNpJweTMrpAQzB7bZsLyhn+e5ipzR1dzELNxwAID4mihOz05nkWdfzpAHdSevmrCPXnh52ay2llbUcPFx9zPB32EcPYHSUoWdyHJkp8fROTSA3K43MlHh6pcSTmZLQ8PiSR5eQX9pyoqm4mCgOHq7SjK+dWFlVLX/5cBtfG9ZT4VAC5/17moZDcJ6/f0/YBMS28msPojFmgrV2hTHmdF/7rbUf++1ix0E9iCIiIh1z8HCVZwyj08u4Pr+MOrfFGBjeK4UeybEs31VMravxc0VCTBTXTx/C6L5pPgNf/a2+vNVbYmw0vVLrg148vTxhrz7w1T/vkRRHdBtmZm0+BhEgNtrgdlu6J8XxwHdP5IwRvfzzZklYeXDhZv78wTbm33gqY7LSQt0c6Yx2L4GnZrWy08DdJUFtTmtCtQ5iAYQuCIqIiEhg9EpJ4Ozcvpyd2xeAIzV1rNpb0hAaF20paFE6VFXn5qF3tzbZ1iMpjszkeHqlxjM4M6kh/PXyDn+pCSTFRft1nFhrk/yM7pfK7OdWcvVTy/jhqYO4ddYI4mO6zsyund3Bw1X845OdnDe2r8Kh+Je1sONDWPQA7P4UTBTYll92kRZ5M+b6uwfxS2vtSZ7HL1trv+O3k3eAehBFREQCa9BtC1qOLfF47afTyEyJp2dyfMjGLx5NVa2Le9/cyNNLdnNCv1TmfW88QzKTQ90s8YM7X1vHf77Yw3u3nE5Oz6RQN0c6A7cbtrwNi+6H/C8hpS9MuwniUuCtX2gMoq/rej0e7Odzi4iISJhqbZ3QrPRETsxOD0GL2i4hNprffHMMpw7L5NaXVnPevMX85oIT+O7E/prtMoLtLqrgP1/s4ZJJ2QqH0nFuF6z/L3zyEBxcD+kD4byHYdz3ISbeOSYmLqxnMW0rfwdE28pjERER6cQ6wzqhXx/dm7duOo2fvbCKW19ew8dbC/jDt3JJS4wNddOkHR56dwsx0YabzhwW6qZIJHPVwpoXYPGfoGgb9BwO33oUxlwE0c2i1NiLIzIQNufvgHiiMaYMpycx0fMYz3NrrU318/UiU5ivkSIiR6F/vyI+tTbGL9LWCe2TlsCz/zOFv3+8nYfe3cKqPSXM+944JgzsEeqmyXFYn1/Ka6vyuf6MIfRK1Qy10g61VbDyX/DpPCjdA31y4btPw6gLICr8SuX9SesgBlsErJES9vQBXUJF/35FupSVe4qZ/fxK8kuquOnMYfx0+tA2zZgqoVO/1EpeSSXGwO+/NYbvTx4Y6mZJJKkuhxVPwWd/hvID0H8ynPYLGPYNiPCS87aOQVRADLY/jYHSvS23p2XDz9YFvz2RRh/QO04Bu32qSuHPJ0FFYct90fEw9EznZzG2m3OL6waxSc62uG6N2xv2JTr7vffFxHf8l4/+fkX86nBVLXe8uo5XV+UzeVAPHr5kHP3SE0PdLPHB11ImibHR3Pvt3IjryZYQqCyBpY/D53+DykMw6DQ4bQ7kfC3ig2E9BUQvYRUQ706n1eGZs1dCD83tc1StBeykTLjs/yAhHRLSnFuUpilvYc2L8PpsqFPAPiproWQ37PkC9n7u3B/cwFGHVvfOhdojjbeaI+CuPb7rmihPWGweNH08jk2EuKTGY+OSIH8lrPgnuGoaz6m/XxG/eOXLfdzx6jpioqP443fGMmtMn1A3SZqZNveDVidK+vS2GSFokUSEikInFC59HKrLYNhMp8cwe3KoW+Z3CohewiogthZw6vUaDSPPg5HnQt8TO803Fh3iqoN9S2Hru7D4oba/Lj61MSx6B8eENEhM972vfntccvvf+0D04LhdUFMO1Ye9bmXNnrdhe1Wp7/PHp8KF/+vU16cP6Ho/d65a+GpNYyDcuxQO73f2xaVA/4kw4GRY9g+oKGj5+tYqAFy1jWGxITxWQk2Fc+8dJms925ocW7+vstl+z+O6qrb9+Uw09J8EqX0hNcuZkju1L6T0g9R+zvOYuPa/fyJdxK7CCmY/v5I1+0r5/pQB3HHuaBLj9GVkOLDWMuj2N33uM8DOuecGt0ES/srynTLSFf90fqeO/iZ87efQd2yoWxYwCohewiogtlYiOeNO50P5pgXOYpvWDan9naA48lwYOBWiu9Asaoe/gm3vOaFwx4dOsDHREBUDruqWxyf1gvMfgaoS59iqUqdUoP5xVWnTfdVlLc/hzURDQqrv8NgkVKY33bdzESy8o2kPXUyiExIHndYsxPkKcq2Eu5rytr1vsUkQn+LjlurcL3302OeIT3OCYp9c6DPGuc8c2TiFc2dQWQL7lsGez2HvF5C3wgljAGkDYMAUyJ7ihMJeoxt7o8OtxNntaho0HxlHq72cOV9zfhmW5Tf9+azXracTFusDY2qWJ0R6PY5P7XpfHog0U1Pn5sF3N/PoxzsY1iuZed8bz6i+moMvlDbkl/GHNzeyeJuPIQCoB1GaKd4Fix+GVf92fo+OvRhO/RlkRs6My+2lgOglrAIiHLuHqaLIWYBz0wLY/r7TS5CQDiPOdsLikBlOOVln4qqDvOVOINy60OnNAUjuA8POcgYGDz4Dtrzjnw/orrrGHrW2hkrvfb4+YLebaQxwRwt3x9yWcuyy2tZ6sFOz4OJnnPf9q7XO7cD6xtAUFeOExD650HtMY4DsFgGz+lnr/DLY+0VjIDy4EbDOFwF9cp0gWB8IU/sd/XzhPMavLWOcrXV+rsv2w2FPYPT1+EhRy/PEJnlCpFfvY0Og9ATJpMyj/xyG8/snchw+2VrALS+uprSyll+fM4orTxmoNROD7EBZFQ+8s5mXvtxHWmIsM0Zk8ua6r6iqdTccozGI0qBgi1OJtuZF5/fUuMucBe57DAp1y4JGAdFL2AXE41FTAds/dMLilregshhiEpyQOPJcGH42JGWEupXtU36wsZdw+wfOh1YT7XxQrw+Fvce07LEIhw+YddU+gmMJvPzD1l9z8b98h7u4pOD1yhxPD5jbBYd2Ng2NX62F8q8aj0nt37K3MT0ntNM/u2ph/xrP2EFPICw/4OyLT3VKLesDYdYEiE8OXVv9zZ89nLVVTpnt4f2NPY+H90NZnidEem7uuqavM9GQ0scrONaHyH5QsAmW/KVpaazGSEoEKyqvZs5La/hg00HOGtWL+y46kR5JKtcOtIrqOh5dtIPHF+3A5bZcNXUgN0wfRlq32IZZTCN5qRXxs6/WwqIHYMNrzmfoiT+AqTce+wvhTkgB0UtEB0RvrjrY85kTFjctcHoKTBQMmOopRT0HuueEupWtc7tg33LY9q4TCvevcrYn94ahX3dC4eDpTrlmpIqEWWo7GrDLC+DA2qahsXCLUxYNzpi9Pl69jL3HOGWasQFah6qyGPYua5xMJm9FYw9v+gDIPtlTMnoy9BrV+ScvCuYXKG63Myazofcx30egzD92iXRMgjP2I7GH0yud2N1z3+xxML9MEWkjay3//GwX9765ifRusfzpknFMG9oz1M3qlFxuy0sr9vLgwi0cPFzNuWP78suZIxmQ0S3UTZNwtHcZfPKAU5UXlwKTfwQnXw/JmaFuWcgoIHrpNAHRm7VOz86mBbBxPhxc72zvnds4brFPbug/TJUXOGWyWxc6vYSVxU6o7T8Zhn3dufXO7TwLjobbGLVgqa10yja9Q+OBdY3BwERDz+HNehvHQpKPD1FHCzjWwqEdTctFCzY5+6JinHPW9w5mT3HKHiX0qsqcsPjXKbQ6RjJ9ABwphprDrZ8nOs4rRPaAbt1bhsjm94ndITroz70+AAAdY0lEQVSm7W0NhwoFiUjr80uZ/dxKdhRW8OPTh3DL14cTG91JfreFgUVbCvjDmxvZ9NVhThqQzq/PHc2Egd1D3SwJN9bCrsWw6H7Y+bHzO+Dk651wmKifFwVEL50yIDZ3aAdsetMJjHuWANb5wFU/I2r2ycf3Iam93C7I+9LTS7gQ8lc5bUnqBUPPcgLhkOmd+x+pPmA63G4o3ukERe/gWJbXeExKX6/QmAsl++Cj3zcN2NHxMOp8pyxx71KoOOhsj09zpqCu7x3MOqnzjc3tbNrSw15X43yRVHkIjhxqel9Z7Hlc3HJf81JXb/FpnjDZ3XeIrO+1zFsBnzyoElhptyM1dfx2/gaeW7qXE7PTmXfpOAZm6P+ljtj81WH+8OZGPt5SQHaPRG6bNYpzcvtovKc0Za1TnfbJA86Xx8m9nTLSCT/oXENJOkgB0UuXCIjeygs8k9zMd8YvuqqdD0Hek9zE+nGR34pC2Pa+Ewq3ve98WDNRzliv+tLRPid2nl5C6Zgjh5oGxq/WOr2A1nX013XPaVoumjlSP1ORJlA97NY6s/02CZY+QmT99spDTm9ldSvLvjQXnwKz5kKPIZAx1On51odTOYo31+7ntpfX4LbwuwvHaAxcOxw8XMWf3t3CC8v2khwfw+wzh3HFKQOJj+nkwwTk6Jp/CT/jDmcIy6IHnMq6tGxn4pnxVwRuaEsEU0D00uUCorfqcqfEc9MCJzRWlTqLag+Z4fQuDp95/DNRut3OgtxbFzqhMO9LwDrT5A/7utNTOGRGZMxwKeGhtsoJiY+d3soBBu4uCWqTJEDCqYfdVetMMlUfHp+a1bbXxadBxmAnLGYM9QRHzy0hLbBtloiRV1LJz55fxdJdh/j2+CzuuXAMyfFBqOSJcJU1Lh7/ZAd//3g7NXVurjhlILNnDKO7Jv8RX18yYgDr/D/8tVsg92Kt63sUCoheunRA9OaqddZY3DjfCYyH852xYQOnNpaipmf7/gA35ExnDOHWhU7gPFIEGGcB8WHfcEJh33Hq0ZGOiYRJfqTzavXnrz9c9QYUbYeibU3vS/fSZFxlUqYnOA5p7HHMGOpMo+7Pyg2JCHUuN3/9cDuPvL+F7B7dmHfpeE7MjuCJ2ALI7ba8sjKPB97ZzFdlVcw6oQ+/PHskg3qqRFc8Wvs/OjED5mzt/JPQ+YECohcFRB+sdXoB62dELdjobE/Lbjl1vYlqnKGyW4YTBod+3ekljNQlNiQ8ddVJfiQ8tOfnr7bKGWvbPDge2t64vAoAxgmaGUO8eh09QTJ9YHDGiEvILNt1iJufX8WBsip+/o0RXHfaYKKiVKZc77NthfxuwUY27C/jxP5p/Prc0UwepCok8VJVCnMHtLJTVUZtpYDoRQGxDYq2O2MWP/gduGpa7o9PhStehX7j1UsogRVOJYjS9fjz56+qzAmKRdu9wqMnQHqPf4yKccbYNilX9YTHlH5N/8/Vv4+IVXqkltv/u4Y3137FqUN78tDFJ9IrtWuPkdp28DD3vrmJ9zcdJCs9kVtnjeD8sf0UnqVR2X74/G+w/KnWZ7lWlVGbKSB6UUA8Dnen43saen07IyLiF9Y6ZfregbGh53FH4zqeADGJnnLVwc4wge3vN/0STz3sEcVaywvL9vKbNzaQGBfN/ReN5cxRvUPdrKArLK/m4fe28NzSvXSLjeanM4Zy9dQcEmJVIigehVvh00dgzQtOVdsJ34LMUbD4QVUZdUBbA6JqWqSptP6tj8EREZGOM8aZCTWpp7Nupze32xkf3hAcPeHx4AbnvrnaSnh9Nuxf7fRC1t/SsjWDXxgyxnDp5AFMzOnB7OdW8sOnl3P11BxuO3tklwhHVbUunli8k//9aDuVtS4umzKAm84cRkZyfKibJuFi7zL49GFn+FNMPJx0JZxygzOOG6D7QFVRBIF6EKUpjQETEQlPrVZ4ADEJTddvBKc81Ts0et+Se2mpjhCrrnPxx7c28+SnOxnZJ4W/fH88Q3ulhLpZAeF2W15bncf9b28mv7SKs0b15vZzRjIkU+vTCZ41DBc6PYa7P4WEdJh8LUy5zvkiTfxGJaZeFBCPk8a4iIiEn6PN8nvzWmdSnOJdULzbc+91O5zf9DUxiZ6wOLBleEwfCHHdAvknES8fbjrIL/5vNRU1dVxwYj8+3VZIfkkV/dITmTNzRMSvofjFjiJ+/+ZG1uwrZUxWKr86ZxRTh+hDv+CUza972QmGBzdAan845adOr6EWtw8IBUQvCogiIhLxOlLhUVsFJXsaA2NJsxBZU970+OTeLUNj/eOUvq1PVqYvGNvlYFkVVzzxBZsPNP17SIyN5t5v50ZkSNxRUM7ctzaxcMMB+qYlOGF3XJYmoBFnje4vn4Elf4WyfdBrtLO4/ZjvQHRsqFvXqWkMooiISGdSH7TaE8BiEyBzuHNrrn7SnIbAuLOxJ3L3Elj7f41LHQFEx0P6gJY9j4Vb4OP7GifZKd3rBFrvtotPvVITOFxd12J7Za2Lu15fR7e4aPqlJ5KVnkh6t1hMGJcHH6qoYd77W3n2893Ex0QxZ+YIrpk2iMS4zj/GUo6hohC+eBSWPgZVJTBwGpz3kLOedhj/THdF6kEUERGR1tXVOGGvedlqfYj0XrLDl7hkp2wssbvvW0Kaeg2AQbctaG2EaRPeYdG5TyCreyL90hLJ6p5I79QEYqODvxxVVa2Lpz/bxV8+3EZFdR2XTh7Az84aTmaKJqDp8g7thCV/gZXPQl01jDwXpt0M2ZNC3bIuRz2IIiIi0nExcZ61GYf43l9Z7ITFx87wvb+m3OlZPFr8iUvxBMb01oOkr9vxztQaxiWw/dITySupbLG9T1oCj18xkbySI+SVVJFfUklecSX5pZWsyyulqKLp2sVRBnqnJjQGyO5eQTK9G/3SE0hJ8F8gt9byxpr93Pf2JvYVVzJ9RCa/OmcUw3p3zgl35DjsX+2ML1z/XzDRcOKlMHW270oGCSsKiCIiItJ+9WEtLbv1SXRuWg1VpU6YrCpx7ivr733cDm5ofOxuWXrZICaxWWhMbz1o7lsOi+5vnO01zEpg58wcwe2vrKWy1tWwLTE2mttmjSS3fxq5/dN8vq6q1uWExpLKhvBYHyRX7S3hrXX7qXU1DeepCTENvZCNAbLxvldKfKtjBV9dmcf972wmv6SSjOQ4usVFs+dQJSP7pPDsD6dw6jBNQNOlWQs7P4bFD8OOD50vf065AU6+HlL7hrp10kZhFRCNMU8C5wEHrbVjfOw/A3gN2OnZ9Iq19p7gtVBERER8OvNO35PonHknREVDtx7O7XhY6/RAthYkG24lzu3QTqj80tlW17I3roXaSph/i3ONHp5e0pR+rU/CE0D1E9HUh6+2zmKaEBvN4MxkBreyZITbbSksr2afV4B0AmUVeSWVLN9dTGllbZPXxEYb+qQlNJStZnmC466iCp76dBfVdc6Y1MJyp/fy0snZ/P7CXKI1AU3X5XbBhtecHsP9q5yJrs66GyZe45SRS0QJqzGIxpjTgHLgmaMExF9Ya887nvNqDKKIiEgQhFMJZ21l017Kf57TttfFJECPwc4tY0hjcOwxBFL6dMrJNMqr6xp6IesDZGOvZBVflVXhcrf+eTErPZFPb5sRxBZL2KithFX/gc/+7Exw1WMITJsNYy89/hJwCbiIHINorV1kjMkJdTtERESkHcZeHBblmoDTexmb2FjW1moJbH+45h0o2g5F2+DQDudx4RZn8W6X1xi/2KTG4Ng8PCb1jNjwmBwfw/DeKQxvZdxgncvNgcPVnDr3A58jSfN9jJ2UTq6yGJb9w5mVtKIAsibA/2/v3oPsLus7jr+/m2y4BHMnCLnHhAjlDoYaEOINgwq2YkHbqWKtiDNesON17ICXWq2dljbSmmJlgs6IRrSKkYCKYoQQ5RJIokAICUiIGENiJBIuSZ7+8fzWPTk5mz27e66779fMb3bPOb/fc77n7HN29rPP73l+r/5UXoCmwxVr211LBcQqvTQi7gM2k0cTf1lpp4i4BLgEYOrUqQ0sT5IktZweT4G9IofE0ZNh5tn7HrN3Tw6VTz7cHRy3PQxPrIEHlu47P/KgUZVHHce/qO+n1raY4cM6/jRHsdJCOkeNOaQJVakpdmyClV+EuxfnU7NnvRrOvCxfsqJN/0Gi/bVbQLwHmJZS2hkRrwW+A8yutGNK6WrgasinmDauREmS1HL6cx3JjmHd13nklfs+tud5+P2vu0Nj19dNd+VVG0uvHXnwGBg/qyw8FmGydH5WK52iW0FPC+l86DVzmliVGmLLA3l+4ZoleW7wcRfki9u/cL8ZYRoEWmoOIkBxiunSSnMQK+z7CHBaSmnrgfZzDqIkSWqY3c/ma0SWBseuUcgdm9jnkh+HTiguIdIBj98Fe0sWjOk8BM5b2FIhsXQV02oX0lEbKf8nxYlvySPm65ZB56FwylvzdU3HeHZeO6p2DmJbBcSIeCHw25RSioi5wPXkEcUDvggDoiRJagnP78qrre4THjfAr1fsO+rYZdiIPFozdkb34jnjZuRLd3hKn2pp9ZL9T8OGPPf2jPfD3He2/enSQ11bLlITEdcB84EJEbEJuALoBEgpLQLeBLw7InYDu4A39xYOJUmSWkbnIXDEsXkr9Ykxlfff8xxs/Bncd92+9x88OofF8uA4bma+xIDhUX3x7FNw08f2D4eQ/xkx/yONr0lN01IBMaX0ll4evwq4qkHlSJIkNcboyT2ssjoFPrA2/+G+/dF8KYFtG4ptI2xela8/l7rnBdJ5aBEcZ3SHxq4wOXqyq0wqz6HddBdsuDVvj9+176JLpf7weCMrUwtoqYAoSZI0JPW4yurl3d9PfHHeyu15PofLrtC4rQiRWx+Ch34Ie57t3rejMy+6Ux4cx83M88qGjzhwnS2+kI56kBJsub87ED56e16FlICjToZ574NVX82XrCg3enKDi1WzGRAlSZKarT+rrHYZ1tkd9srt3QtPbS4Jj8Xo4/aN8OiKIiQUoiM/b3lwHDczh8oHlu4bYnc8lm+X1q/WsWMTbPhpdyj845Z8//hZcOKbYcbZMONl+RRSgInHHPifFBoyWm6RmnpwkRpJkqQyKeURo67gWH766q5t++4fHZUX0uk6DVbNtev38Mht3YHwyYfy/SMPh5nz8zbjbBgzpec2HCEe1NpykRpJkiQ1SAQcNjFvU0/f//Fd23NQ7AqOP/6nyu3seCyvyDr+RfWtV/va/Sw89vPuUcLN9+QA3zkSpp8Bp709h8KJx1a/aNEJFxoIZUCUJElSBYeMhUljYdIp+fbd11ZeSAfgC6fA+NkwZwEcfS5MOR2G+WdmTe3dC79dUzKP8A7YvQtiGEw+Dc76UA6Ek07rfS6pdAB+ciVJktS7nhbSecXleWXUB5fBykWw4gs5XM56dQ6Ms16VL8uhvtv+SHcg3Lgcnn4y33/4i+HUt+VAOO0MOHhU00rU4GNAlCRJUu96W0jn9HfBM3+Ah38M626CdTfDmiXQMRymzcsji3MWVF5MR9nT22BjycIy2x/J97/gSJj9mmIe4Vkw6simlajBz0VqJEmSVHt798CmO/PI4rqb4HcP5PsnzIE55+Zt8kuG1nUZyxeBmf9RGHVUdyD8zWogwUGjYPrLuheXmTC7+nmEUg+qXaTGgChJkqT627Yhjyo+uCxfh2/vbjh0PMw+B45eAC96xeA+VXL1ErjhfXneYLmOzjxvc+b8vB11snM4VXMGxBIGREmSpBbyzA5Y/yN48CZ46AfwzO9zSJp+Zh5ZPHoBjJ3W7Cr7LqVi9dcNeWXXP102ZEP3KqPlDp0Al62GESMbX6+GFC9zIUmSpNZ08Gg47oK87dmdL9ewblkOjMs+nLeJx+agOOdcmHRq65yKmlJeLKY8AG4rbj+zo2TnyNeJHDejcjiE3JbhUC3EEURJkiS1jicf7p63+OgKSHvyKNvRr+k+FfWgw+pbQ0qwc8v+4W/bhnxtyGf/0L1vdBQhcGa+FuS4md3b2Okw/KC835XHVb5MyOgp8IG19X09Ep5iug8DoiRJUhvatR3W35ID4/of5tG5YSPySp5HL8jbmCnd+5cvAlO6ymq5lOCpJyoEwCIEPreze98Ylk95LQ1/44owOGZqddcdXL2k8mVCzlvoxenVEAbEEgZESZKkNrfnefj1ymJ0cVkOcgBHHJ8vn9HRCbdfuX8Ae+Un4Yhj9g2AT26A7Rvh+ae79+0Ynkf8ygPguBk5BA7rHPhr6EuAlWrMgFjCgChJkjSIpARbH+qet/jYyp7n+JUaNqIIgSXhr+vU0FGTXTlUg5qL1EiSJGlwioDDj87bGe/PF5j//Iye93/rd3MQHDWpdRa7kVqUAVGSJEnt7dBxebGXnhaBmTm/0RVJbauj2QVIkiRJA/bKy/Ocw1Kdh+T7JVXNgChJkqT2d8KFeUXQ0VP40/UHXSFU6jNPMZUkSdLgcMKFBkJpgBxBlCRJkiQBBkRJkiRJUsGAKEmSJEkCDIiSJEmSpIIBUZIkSZIEGBAlSZIkSQUDoiRJkiQJMCBKkiRJkgoGREmSJEkSYECUJEmSJBUMiJIkSZIkwIAoSZIkSSoYECVJkiRJgAFRkiRJklQwIEqSJEmSAAOiJEmSJKlgQJQkSZIkAQZESZIkSVLBgChJkiRJAlosIEbENRGxJSLW9vB4RMTCiFgfEasj4pRG1yhJkiRJg1VLBURgMbDgAI+fC8wutkuALzagJkmSJEkaEloqIKaUlgPbDrDLG4CvpGwlMCYijmxMdZIkSZI0uA1vdgF9NAl4rOT2puK+35TvGBGXkEcZAXZGxIMHaHc0sKPKGqrdt5r9JgBbq3zedtWX97ae6llHLdseSFv9ObbWfd9+nw2Ffl/L9tu931ez31Do99Aafd9+P/Bj/Funb+z3jWurr8fa7/c1raq9UkottQHTgbU9PPZ94MyS27cAp9bgOa+u9b7V7Afc1ez3uwE/z6rf23ato5ZtD6St/hxb675vv699n2jlOmrVfrv3+2r2Gwr9vpZ9opVrsN/3bb+h0Pft941rq6/H2u/7t7XUKaZV2ARMKbk9Gdhcg3a/V4d9+9LmYNYq70M966hl2wNpqz/H1rrvt8rPu9la5X2odx21ar/d+31/6xiMWuF9sN8P/Bj7fd+0wvvQLv1+oG319Vj7fT9EkXJbRkRMB5amlI6r8NjrgPcArwVOBxamlOY2tMAaioi7UkqnNbsOqZHs9xqK7Pcaquz7Goravd+31BzEiLgOmA9MiIhNwBVAJ0BKaRFwIzkcrgeeBt7enEpr5upmFyA1gf1eQ5H9XkOVfV9DUVv3+5YbQZQkSZIkNUe7zUGUJEmSJNWJAVGSJEmSBBgQJUmSJEkFA2ILi4iREXF3RLy+2bVIjRARx0TEooi4PiLe3ex6pEaIiL+IiC9FxHcj4pxm1yM1QkTMjIgvR8T1za5Fqqfi7/lri9/zf9PseqphQKyDiLgmIrZExNqy+xdExIMRsT4iPlpFUx8BltSnSqm2atHvU0r3p5QuBS4E2nZ5aA0dNer330kpvRO4GLiojuVKNVGjfr8hpfSO+lYq1UcfPwNvBK4vfs+f3/Bi+8GAWB+LgQWld0TEMOC/gHOBY4G3RMSxEXF8RCwt2yZGxKuAXwG/bXTxUj8tZoD9vjjmfOA24JbGli/1y2Jq0O8L/1gcJ7W6xdSu30vtaDFVfgaAycBjxW57Glhjv7XUdRAHi5TS8oiYXnb3XGB9SmkDQER8HXhDSumzwH6nkEbEy4GR5A62KyJuTCntrWvh0gDUot8X7dwA3BAR3we+Vr+KpYGr0e/7AD4HLEsp3VPfiqWBq9Xve6ld9eUzAGwih8R7aZPBOQNi40yi+78HkDvL6T3tnFL6OEBEXAxsNRyqTfWp30fEfPKpGAcBN9a1Mql++tTvgfcCrwJGR8SslNKiehYn1Ulff9+PBz4DnBwRHyuCpNTOevoMLASuiojXAd9rRmF9ZUBsnKhwX+rtoJTS4tqXIjVMn/p9SulW4NZ6FSM1SF/7/ULyHxBSO+trv38SuLR+5UgNV/EzkFL6I/D2RhczEG0xzDlIbAKmlNyeDGxuUi1So9jvNRTZ7zUU2e811A2az4ABsXHuBGZHxIyIGAG8GbihyTVJ9Wa/11Bkv9dQZL/XUDdoPgMGxDqIiOuAO4A5EbEpIt6RUtoNvAe4GbgfWJJS+mUz65RqyX6voch+r6HIfq+hbrB/BiKlXqfBSZIkSZKGAEcQJUmSJEmAAVGSJEmSVDAgSpIkSZIAA6IkSZIkqWBAlCRJkiQBBkRJkiRJUsGAKEmqu4i4MiIuK7l9c0T8b8ntf4uIf+iljRVVPM8jETGhwv3zI2JeD8ecHxEf7aXdoyLi+uL7kyLitX08/uKIuKr4/tKIeGtvr6W319DfduqhqG1ps+uQJA3c8GYXIEkaElYAfwX8R0R0ABOAUSWPzwMuq3Rgl5RSxYBXpfnAzqKO8nZvAG7o5bk3A28qbp4EnAbcWO3xZW0tqnbfMvMpeQ0DaEeSpB45gihJaoTbySEQ4M+AtcBTETE2Ig4CjgFWAUTEhyLizohYHRGf7GogInYWXzsi4r8j4pcRsTQiboyIN5U813sj4p6IWBMRL46I6cClwAci4t6IeFlpYWWje4sjYmFErIiIDV3tRsT0iFgbESOATwEXFW1dVHb8eRHx84hYFRE/iogjyt+IiPhERHywGJW8t2TbExHTKrVR6TV0tVO0eVJErCzes/+LiLHF/bdGxL9ExC8iYl35ay/2OTIilhftru3aJyIWFO/jfRFxS3Hf3OK9WVV8nVOhvZERcU3xM1wVEW/osVdIklqOAVGSVHfFCNzuiJhKDop3AD8HXkoejVudUnouIs4BZgNzySN1p0bEWWXNvRGYDhwP/H3RRqmtKaVTgC8CH0wpPQIsAq5MKZ2UUvpZL+UeCZwJvB74XNnreA64HPhG0dY3yo69DfjzlNLJwNeBD/f0JCmlzUUbJwFfAr6VUnq0UhtVvIavAB9JKZ0ArAGuKHlseEppLnmE9gr299fAzUUdJwL3RsThRU0XpJROJI/+AjwAnFXUdjnwzxXa+zjw45TSS4CXA/8aESN7eh8kSa3FU0wlSY3SNYo4D/h3YFLx/Q66T/08p9hWFbcPIwfG5SXtnAl8M6W0F3giIn5S9jzfLr7eTQ6TffWdou1fVRoB7MVk4BsRcSQwAtjY2wERcQY56HaN7vWpjYgYDYxJKf20uOta4Jslu5S+H9MrNHEncE1EdJJf+70RMR9YnlLaCJBS2lbsOxq4NiJmAwnorNDeOcD5XaObwMHAVOD+A70OSVJrcARRktQoK8iB8HjyKaYryaN/88jhESCAz3aNrKWUZqWUvlzWTvTyPM8WX/fQv3+EPlvyfW/PVe4LwFUppeOBd5HDUY+KEPhl4KKU0s7+tFGFA74fKaXlwFnA48BXi4VvghwAy30a+ElK6TjgvB5qC/LIY9fPcGpKyXAoSW3CgChJapTbyadtbksp7SlGpcaQQ+IdxT43A38XEYcBRMSkiJhY1s5twAXFXMQjyIu39OYp4AU1eA29tTWaHLQA3nagRooRuyXkU0PXVdFGxedNKe0AtpfML/xb4Kfl+x2gjmnAlpTSl8hh9RTyz+PsiJhR7DOuQm0X99DkzeR5oFEce3K1tUiSms+AKElqlDXk1UtXlt23I6W0FSCl9APga8AdEbEGuJ79Q9G3gE3kUcj/Ic9l3NHLc38P+MtKi9T0w0+AY7sWqSl77BPANyPiZ8DWXtqZB7wE+GTJQjVHHaCNA72Gt5Hn+q0mz938VB9ez3zyvMNVwAXAf6aUfgdcAnw7Iu4DuuZafh74bETcDgzrob1Pk089XR0Ra4vbkqQ2ESlVOoNEkqTWFRGHpZR2RsR44BfAGSmlJ5pdlyRJ7c5FaiRJ7WhpRIwhL+LyacOhJEm14QiiJEmSJAlwDqIkSZIkqWBAlCRJkiQBBkRJkiRJUsGAKEmSJEkCDIiSJEmSpIIBUZIkSZIEwP8D4Cke4iGUr/gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n",
    "\n",
    "## Answer:\n",
    "\n",
    "在上述网络结构中，weight_scale为接近0.1最为合适"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "在有正则化的情况下，batch_size的大小对训练集的正确率影响很大，但是对验证集的正确率影响微小。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "bn:1,2,3\n",
    "ln:4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_forward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "  means:  [ 4.81096644e-16 -7.40148683e-17  2.22044605e-16 -5.92118946e-16]\n",
      "  stds:   [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "  means:  [5. 5. 5. 5.]\n",
      "  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
      "image/png": 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QQggx7AWbm3Hv3h3phfOfOw+AZcwYku66C/viRdgXLsSSmRnlSkcnz8cfX7c5h8PBYAJdPnChy/WLQEmPNlMAlFIfYQzLXKu13tzzQEqpbwDfACgsLBxESUIIIYQQQgwt7fPh+eSTyGImnsOHIRTClJhI4vz5pD/+BPbFi4grKhrypfnF5YbznMNrMZhA19tvo+5x3QJMBu4ECoAPlVIztdbObg/S+vfA7wGKi4t7HkMIIYQQQohhQ2uNr7zc6IH7aCdte/ca+7eZTOHtBJ7CvmgRttmzUXFx0S5XjHCDCXQXgbFdrhcAVb20KdNa+4EKpdQJjIC3dxDnFUIIIYQQYkgF6uu7bydw6RIA1nGFpKxehX3RIuwlJddtNUchBmowgW4vMFkpNQGoBB4Beq5guQF4FPh3pVQmxhDMM4M4pxBCCCGEEDecsZ3A/sg8uPYTJwAwOxwkLlyIfdFC7IsWE1fQ93YCQgyFaw50WuuAUuo7wBaM+XH/prU+qpT6KbBPa10avu8epdQxIAj8QGvdcD0KF0IIIYQQ4nrRodDl2wn4fMZ2AvPmkfW973VuJ2A2R7tcISKU1sNrylpxcbHet29ftMsQQgghhBAjnL+yktZwgGvbVUbQaSzzED9lijGEcvEiEouLZTsBMeSUUvu11sUDaTuojcWFEEIIIYSIFcGWFto6thP4aCe+c+cAsGRlkXTHHZ3bCcTg5tJi9JJAJ4QQQgghRiTt9+M5dMhYyGRneDuBYBCVmIj91ltJe/wx7IsWETdxomwnIGKWBDohhBBCCDEiaK3xVVREAlzbnj2E3G4wmUiYNZOMbzxJ0qJF2ObMke0ExIghgU4IIYQQQsSsQEMD7l1lkcVMAjU1AFgLC0l54DOd2wk4HFGuVIgbQwKdEEIIIYQYlvy1tVQ+830KfvubyLy2kNfbuZ3Arl20Hz8OgMnhwL5gAfann8a+eBFxBQXRLF2IISOBTgghhBBCDEv1v3sBz/79VP/8b7DNnGHMg9tvbCeA1Uri3Llk/c//iX3xIhKmT5ftBMSoJIFOCCGEEEIMO627duF8+WXQmtYtW2jdsoX4KVNIe/TRzu0EEhOjXaaIURvPbGTdgXXUuGvIseewZt4a7i+6P9plXRMJdEIIIYQQYlgItbfTsnkzTS+ux/Pxx513mM04HniAvF/+InrFiRFj45mNrN25Fm/QC0C1u5q1O9cCxGSoM0W7ACGEEEIIMbr5zp3j0t/9mtN33EnVD58lUFcHli79DsEgzZs2GbcLMUi/3f/bSJjr4A16WXdgXZQqGhzpoRNCCCGEEENOBwK0vvceTS+ux/3RR2A2k7x0KWmPfJHmt9/G+epr3duHQtT97gVyf/J8lCoWscgf8nOq6RSH6g4ZP/WHuNR2qde2Ne6aIa7u+pBAJ4QQQgghhoz/Ui3OV17G+fIrBGpqsGRnk/k/vkPqQ1/Amj0GgEt/92vw+3s80I/n4MEoVCxiSW1bbSS8fVL3CccajkV64zISMpiTNYdGTyMt/pbLHptjzxnqcq8LCXRCCCGEEOKG0qEQbWVlNL24npbt2yEYxL54MTl/9b9IuvNOlKX7R9KiDa9HqVIRS9qD7RxvOB7peTtUd4hqdzUAVpOVaRnTeGjKQ8zJmsPsrNnk2nNRSl02hw4gwZzAmnlrovVUBkUCnRBCCCGEuCGCTifO1zfgXL8e37lzmFNTSf/yl0n74sPEjRsX7fJEDNFaU9la2S28HW88TiAUACDPnsecrDl8afqXmJ01m2np04gzx/V6rI6FT0bKKpdKax3tGropLi7W+/bti3YZQgghhBDiGmit8R46RNOL62netAnd3o5t7lzSHn2E5BUrMMXHR7tEEQPcfjdH649yqN4YOnmo7hCN3kYAbBYbMzJmMDtrtvGTOZusxKwoV3x9KaX2a62LB9JWeuiEEEIIIcSghdracL31Fk3r19N+7DimxEQcn32QtEceIWHq1GiXJ4axkA5x1nXWCG7h3rfTztOEdAiA8SnjuS3/tsjQyUmpk7CYJMZ0kFdCCCGEEEJcs/ZTp2h6cT2u0lJCra3ET5lCzk+eJ+WBVZiT7NEuTwxDrnZXt6GTh+sORxYpSY5LZnbmbJYWLmV21mxmZc7CEe+IcsXDmwQ6IYQQQghxVUI+Hy1vv0PT+hfx7NuPslpJvnclaY88gm3uXJRS0S5RDBOBUIDTztN8UtvZ+3a2+SwAJmVicupkVk5YGRk+OT5lPCYlW2VfDQl0QgghhBBiQHwXL+L840s4X32VYGMj1rFjGfODv8Dxuc9hSUuLdnliGKhrq4sEt0N1hzjacBRPwANAekI6s7Nms3rSauZkzWFGxgwSrYlRrjj2SaATQgghhBB90sEgre9/QNP6F3F/uAOUIumuu0h75BHsixehTNKbMlr5gj6ONx7v3LS77hBV7ioALCYL09Kn8bnJn2N2ptH7lp+UL723N4AEOiGEEEIIcZlAXR3OV1+l6aWXCFRVY8nKIvPpb5L6hS9gzc2NdnliiGmtqXJXdQtvxxuP4w8ZG8Dn2nOZnTWbx6c9bmwbkDGNeLOsaDoUJNAJIYQQQgjA+NDetmcvTetfpOWdrRAIkLhwAdk/fJbku+9CWa3RLlEMkTZ/G0cbjka2DDhUd4gGbwNgbMI9I3MGT0x/gjmZc5iVNYsxiWOiXPHoJYFOCCGEEGKUCzY349rwBk1//CO+8nJMDgfpjz9O6he/SHzRhGiXJ26wkA5xtvlst963U85T3bYNWJy/ODJ0clLaJKwmCffDhQQ6IYQQQohRynP4CE3rX6R545/QXi8Js2eT+7d/S8p992JKSIh2eeIGcbW7OFJ/pLP3rf4QLb7wtgHWZGZlzeKuwruYnWlsG5CakBrlikV/JNAJIYQQQowiIY+H5j/9iaYX1+M9cgRls+F44AFSH/kithkzol2euEYbz2xk3YF11LhryLHnsGbeGu4vup9AKEC5s7xbeKtwVQCgUExKm8Q94+6JbNo9wTFBtg2IMUprHe0auikuLtb79u2LdhlCCCGEECNK+5kzNK1fj2vDG4Sam4mbNJG0Rx7FsXoV5uTkaJcnBmHjmY2s3bkWb9Abuc2szBQmF1LTVtN924DwsMnZWbOZmTkTu1U2fx+OlFL7tdbFA2krPXRCCCGEECOU9vlo2baNphfX07ZnD1itpCxfTtqjj2ArLpYl5EeI3+z7TbcwBxDUQS62XuQLU74QCXAFSQXyno9AEuiEEEIIIUYYf1UVTS+9hPOVVwnW12PNyyPre98j9fOfw5KZGe3yxHVQ76lny9ktbK7YTK2nttc2gVCA50qeG+LKxFCTQCeEEEIIMQLoUAj3jh00vbie1vffB61JuuMO0h59BPttt6HM5miXKAapydvE1vNb2Vyxmb01e9FopqRNIdmaTIu/5bL2OfacKFQphpoEOiGEEEKIGBZobMT56qs4//gS/osXMWdkkPHkk6Q9/AWs+fnRLk8MUouvhe3nt7Pp7CbKqsoI6iDjU8bz1JynWDl+JRNTJ/Y6hy7BnMCaeWuiWLkYKhLohBBCCCFijNYaz/79NL24npa330b7/STeeitjnvkeycuWoeLiol2iGIQ2fxvvXXiPzWc3s6NyB/6Qn/ykfL4848vcO+Febkq7qdtcuPuL7gfodZVLMfLJKpdCCCGEEDEi2NqK6403cK7/I+2nTmFKTsbx4IOkPfJF4idOjHZ5YhC8AS87Knew+exm3r/wPt6glzG2Mdwz/h7unXAvszJnyYImo4iscimEEEIIMYJ4jx+n6cX1uN56C93WRsKMGeT+/Gek3HcfpsTEaJcnrpE/6GdX9S42V2xm+4XtuP1u0hPSWT1pNSvHr2Re9jzZE05ckQQ6IYQQQohhKOT10rxpM03rX8T7ySFUfDwp999vbDkwa1a0yxPXKBAKsLdmL1vObuGdc+/Q7GsmOS6ZFeNXsGL8CubnzMdiko/oYuDkt0UIIYQQYhjxnT1L0/o/4nr9dYIuF3ETJpD93LM4HnwQs8MR7fLENQjpEAdrD7KpYhPvnHuHRm8jiZZE7i68m5XjV7IobxFWszXaZY4qGw5W8ustJ6hyeshLtfGDFTfx4NzYXERoUIFOKbUSWAeYgT9orX/Z4/6vAL8GKsM3/aPW+g+DOacQQgghxEijAwFatm/HuX497p27wGIhedky0h55hMSS+TJ3KgZprTlSf4TNZzez+exmattqSTAnsKRgCSsnrOT2/NtJsCREu8xRacPBSp577TAefxCASqeH5147DBCToe6aA51Sygz8E7AcuAjsVUqVaq2P9Wj6R631dwZRoxBCCCHEiOCvraXyme9T8NvfYMnKwn/pEs6XXsb58ssEamux5OaStea7OD7/eaxjxkS7XHGVtNacbDrJpopNbD67mcrWSiwmC7fl38b3b/k+d469k0SrzHmMphavn59vPBYJcx08/iC/3nJidAU6YD5wWmt9BkAptR5YDfQMdEIIIYQQAqj/3Qt49u+n6sfPoyxmWt99D0Ih7LfdRs7an5C0ZAnKIjNiYs0Z1xk2Vxg9cRWuCszKzILcBTw1+ynuLrwbR7wMlY2G2mYvR6uaOVrl4lh1M0ermjnX0NZn+yqnZwiru34G8y9GPnChy/WLQEkv7T6vlFoCnAS+p7W+0LOBUuobwDcACgsLB1GSEEIIIcTwoLUm2NSEv7ISf2Ul3hMncb78MmiN+733MDkcZPz5V0l9+GHixo6NdrniKl1oucCWs1vYXLGZE00nUCiKc4p5YtoTLBu3jPSE9GiXOGqEQppzjW1GcKtqDoe4Zupb2yNtxmUkMiMvhYeLx/JvOypocPsuO05eqm0oy75uBhPoehvM3XNTuzeBF7XW7UqpbwL/D7j7sgdp/Xvg92DsQzeImoQQQgghhoQOhQjU1xOoqsJXWYm/qioc3qqMy1VVaE8f3/ibzSSvXMGY739/aIsWg1LjromEuCMNRwCYkzWHZ+c/y/JxyxmTKMNkbzRfIMSp2haOVjWHw5uL49UttLYHALCYFJOzk7ljShYz8lKYkZfCtLwUUhI6F53JT7V1m0MHYLOa+cGKm4b8+VwPgwl0F4GuXycVAFVdG2itG7pc/VfgV4M4nxBCCCHEkNHBIIG6uu5BLdzbFglsvu7f8ptTU7Hm5RFfVETSbbdhzc/HWpCPSkjg4tPfQreHewyCQZo3vMGY73wHS1ZWFJ6dGKh6Tz3vnHuHzRWbOVB7AIBp6dN45pZnWDF+BXlJeVGucORqbQ9wvLqZo5WuSK/bqdoW/EGj/ycxzsy03BQ+Ny8/HN4cTM5OIt5i7ve4HfPkZJVL2AtMVkpNwFjF8hHgsa4NlFK5Wuvq8NVVwPFBnE8IIYQQ4rrRgQCBS5fwd/SwRXrZwj1s1dXg93d7jDkjwwhsU6eStPRurHl5RmjLy8Oal485yd7ruarX/jU6FOp+/lCIut+9QO5Pnr9hz1FcG1e7i63ntrLp7Cb21uwlpENMSp3Ed27+DisnrGRcyrholzji1LW0c7TK1a3n7WyX+W4Z9jim56WwZEpRpOdtfIYdk+naVoB9cG5+zAa4nq450GmtA0qp7wBbMLYt+Det9VGl1E+BfVrrUuC7SqlVQABoBL5yHWoWQgghhLgi7ffjr6np3rtW1aWX7dIlCHZf6c6SlYU1Lw/bzJmkrFiBNb9rYMvDZLu2OTaejz++LBzi9+M5ePBan564zlp9rbx74V02VWxiV9UuAjpAYXIhX5/1dVaOX8nktMnRLnFE0FpzvrGtc7GScM9bbUvnfLex6TZm5Dr4/LwCZuSnMD3XQXZKvGzf0Qel9fCaslZcXKz37dsX7TKEEEIIMcyFfL7L56919LBVVhKorYWuvWJKYcnO7uxVy8+LXI7Lz8eSm4spPj56T0gMuTZ/Gx9UfsDmis18ePFDfCEfufZcVo5fycoJK5mWPk1CxCD4gyFOXWoNrzBp9L4dr2qmJTzfzWxSTB6TxPS8FKbnGkMmp+el4LDJJutKqf1a6+KBtJV1cYUQQggxLIW83r7nr1VWEqir6/4AsxlrdjbW/HzsJSWdoS0/3/jJzkbFxUXnyQAbz2xk3YF11LhryLHnsGbeGu4vuj9q9YyeGKEkAAAgAElEQVRWvqCPHZU72Fyxmfcuvocn4CHLlsUXbvoCK8evZHbWbEzKFO0yY467PcCnNeEVJiubOVrt4mRNK76g8aWKzWpmWm4yq+fmMSPPwYy8FKZkJ5Ng7X++m7gyCXRCCCGEuGY9N8q+GiG3u+/5a5WVBBsauj/AYsGam2sEtttvD/ewGaEtLj8fS3b2sN3DbeOZjazduRZv0AtAtbuatTvXAkioGwL+kJ/d1bvZVLGJd8+/S4u/hdT4VD5T9BnunXAv88bMw2ySYDFQDa3tkUVKOoZNVjS46Rj4l5ZoZUaeg68uHs/08GIlEzLtmK9xvpvo3/D8V08IIYQQMaFjo+zeFvcItrR09rBd7DksspKg09mtvbJaI0MgE+6+q7NnLXybJSsLZY7ND93rDqyLhLkO3qCXX+39FQXJBTjiHKTGp5IclyzB4joJhoLsv7SfTWc3sfXcVpztTpKtydxdeDf3TriX+bnzsZpkaF9/tNZcbPJEhkt2LFhS09z5u5yfamNGXgqrbzZWmpyel0KuI0GGqg4hCXRCCCGEuCb+S7W4XnsNtMb58svogJ9gY1MktIWam7u1VwkJnYFt5szL5rFZMjNRppEz1K3R28ie6j2UVZdR7a7utU2Tt4kn/vRE5LpCkRKfEgl4jngHjvj+L6fGp5JoSZQP0EBIh/ik7hM2V2zm7XNvU++px2axcefYO7l3/L0szl9MnDl6w26Hs0AwxOm6Vo5WNkfmvB2raqbZa8x3MymYNCaJhRMzjOCWa4S31ER5PaNNAp0QQgghBsxfU0Pb7t24y3bTvGVL5z5sgQCu114nvmgC1rx8EufN675CZH4+5vT0ER062vxt7L+0n93VuymrLuNE0wkAkqxJxJvjaQ+2X/aYzIRM/nrxX+Nqd+Fqd+Fsd+Jsd0au13vqKXeW4/K5cPvdfZ7bYrL0GgJT41NJiU+JXO4ZCuPNsb8IjNaaY43H2Fyxmc1nN1PjriHOFMeSgiWsnLCSJQVLsFmubXXSWLLhYOWA91Vr8wX4tKZjc26j9+3TmhZ8AWO+W4LVxNScFD4zJy+yv9vUHJnvNlzJKpdCCCGE6FOgsZG2PXtwl5XRVrYb39mzAJhSUgi1tnZbRVLFxzNp6zujZqNsf8jPkfojlFWXUVZVxqH6QwRCAawmK3PHzKUkt4QFuQuYnjGdLWe3dJtDB5BgTmDtorUDnkPnD/px+boHv+b25stCoLPdabTzGpd9IV+fx7RZbEbI66NHsOd/HfEOUuJSsJii3ydwqukUmyo2sfnsZi60XMBisrA4bzErxq/grrF3kRSXFO0Sh8yGg5U899phPP7ObThsVjO/+Nws7piSFZnr1vHfino3oXAEcNiskX3dOhYrmZBpx2IeOb3lsehqVrmUQCeEEEKIiGBrK21799JWtht3WRntJ4xeJlNiIom33kriggXYF5TQ9Mc/4nz1te57q1mtpD700IjdKFtrzSnnqUgP3L6afbQF2lAopmVMiwS4uWPm9tojFI1VLrXWeIPezqDXz3+7Xfa5COlQn8dNjkvuDIEJnYGwZ49g1+tJ1qSr6qHt7fWakTGDzWc3s7liM+WuckzKRElOCSsnrGRp4VIc8Y7r8bLFnMW/3E6l03PZ7SZFJLgB5DkSmB4ObTPyUpiR7yBP5rsNSxLohBBCCDEgIY8Hz8GDuMt2495dhvfIUQgGUfHx2ObNxV5iBLiEGTNQ1s4FJM48+FnaP/30suPFT51K0YbXh/Ip3FDVrdVGD1x1Gburd9PgNVbeHJcyjpKcEhbkLeDW7FtJTUiNcqXXV0iHaPW34vIa4a7XXsBeLrf4W/o8plmZu/cAxvU9P/BQ3SH+5dC/dBumqlBoNArFvOx5rBy/kuXjlpNhyxiKl2RY0VpzodHD4UoXR6pcvPBeeZ9tf3TfVGN/t9wU0uwy3y1WSKATQgghRK+0z4fn8OHIEErPxx+j/X6wWLDNno19QQmJJQuw3TxnVG6y7fQ62VOzJ9ILd77lPAAZCRmRHrgFuQvITcqNcqXDUyAUiPTwudpdOL3ObsNE+wqDPVcA7UtKXAqvrnqVHHvODX4mw0copKlocHOk0hX+aeZIlYuW8GIlVrPRu+YPXv6ZPj/VxkfP3j2k9YrrQzYWF0IIIQQAOhjEe/xT2naX4S7bTdv+/ei2NlCKhGnTSPvSl7AvKME27xbMSfZolzvkPAEPBy8dpKzGmAf3aeOnaDR2q53i7GIenfooJbklTEqdJMPSBsBispBhy7jqXjNvoHNYaLOvmT/f8ue9tmvxtYzoMBcIhiivC4e3KiPAHatqxu0z5sbFWUxMy0lm1Zw8ZuY7mJnnYEpOEpsO1/Q6h+4HK26K1lMRQ0gCnRBCCDGCaK3xnT4dGULZtmdvZPuAuEkTSf3sZ0lcUIL91lsxp46sYYIDEQgFONZwLDKE8mDtQfwhPxaThTlZc3j65qdZmLuQGZkzZI+yIZRgSSDBkkC2PRuAXHtur1s9jKQw5wuEOFXbwtHK5sjQyePVzXj9xtxFm9XM9LwUHrqlgBn5DmblO5g0JglrL4uVdKxmOdBVLsXIIoFOCCGEiGFaa/wXLkSGULp37ybYYMzzso4dS8qKe0gsWUDi/FuxjhkT5WqHntaaClcFu6p3sbt6N3tr9tLqbwVgavpUHp/2OCW5JcwbM49Ea2KUqxUd1sxb0+uqoGvmrYliVdfO6w9yoqYl3OvWzJFKFydqWvAFjfCWFG9hel4Kj5eMY2Z+CjPzHBRlJWE2DbxX+MG5+RLgRikJdEIIIUSM8V+6FNkLzl22i0CV0ZNhycrCvmhRZB5cXMHo/HBX465hd/XuyE+tpxaAgqQCVoxfwYK8BczPmU96QnqUKxV96Vj9c6hXBb0ePL4gx6qbO+e8VTVz6lILgfBykw6blZn5KXx18Xhj2GS+g3HpiZiuIrwJ0ZUsiiKEEEIMc4GmJtp27zGGUJbtxldRAYDZ4SCxpMQYQrlgAXETJozKeV7Nvmb21uylrMpYjfJs81kA0hPSmZ8znwW5CyjJLaEguSC6hYoRp8Xr51hVM0eqmjla6eJwpYvyutbIVgEZ9rhwaDN63WbmOyhIs43Kv1NxdWRRFCGEECKGddsLbvfuyPYAHXvBpT78MPYFJcTfdBPKNPo2/20PtvNx7ceReXBHG44S0iFsFhu3ZN/CQ1MeYkHuAianTcakRt/rI24MV5ufo1Wu8Hw3I8CdqXdH7s9OiWdmnoN7Z+UyKxziclJkjzdx40mgE0IIIaIs5PVG9oJrKyvDc+SIsRdcXBy2efPI+p9rSCwpwTZzZre94EaLYCjIp42fRubBHaw9SHuwHYuyMCtrFk/NfoqS3BJmZ87Gah59r4+4/hpa2zlS1XXYpIsLjZ0bd+en2piZn8Jn5+YzM9/BjPwUxiQnRLFiMZpJoBNCCCGGmPb7u+8Fd/Bg515ws2aR8Y0nsZcswDb35lG5F5zWmnPN5yJ7we2p2UOzz1ipc3LaZB6+6WEW5C7gluxbsFtH31YL4vqqbfYavW7h/d2OVrqocnUuxjIuI5HZ+ak8Nt9YsGRGnoN02aBbDCMS6IQQQogbTPaCu7J6Tz1l1cZecLtrdlPjrgEgz57HsnHLKMkpYX7ufDJtmVGuVMQqrTVVLi9HKl2R+W5Hqpqpa2kHQCmYkGnn1gnpzMwzet1m5Dlw2KTXVwxvEuiEEEKI60xrja+83Ahvu8tw79lLyOUCIG7iRFIffJDEBSUk3norlrS0KFcbHa2+VvZd2heZB3faeRoAR7yDkpwSnpz1JAtzF1KQXCBzkMRV01pzodET2d/tSKWLo1XNNLp9AJgUTB6TzO2TM8Pz3RxMy00hKV4+GovYI7+1QgghxCBprfFfvNh9L7j6egCsBQUkL1+GvWQBiSXzR+VecAC+oI9P6j6JBLgj9UcI6iAJ5gTmZc9j1cRVlOSWMDV9qixkIgDYcLByQBtlh0KaigZ353y38NDJFm8AAKtZMSU7meXTso3VJvMdTM1JwRZnHuqnJMQNIYFOCCGEuAb+S7WdQyjLyvBXVQFgzsrEvnBheC+4EuIKRu5S+RvPbOxzn7CQDnGi8URkHtyB2gN4Ah7MysyMzBl8bdbXWJC7gDlZc4gzy3wk0d2Gg5U899phPP4gAJVOD8+9dphQKMTMglQOX3SF57s1c7TKhdtntIuzmJiWk8wDc/KMnrc8B1Nykoi3SHgTI5fsQyeEEEJ04a+tpfKZ71Pw299gycqK3B5oaqJtz95IiPOdOQOAyeHAPn9+515wRUWjYojgxjMbWbtzLd5g5+IR8eZ47ptwH26/mz01e3C2OwGY6JjIgrwFlOSUUJxTTHJccrTKFjFi8S+3U+n09NvGZjUzPS+FmXkpzMh3MCvfwaQxSVjN0sMrYp/sQyeEEEJco/rfvYBn/35q1/0DycuWdu4Fd/w4YOwFZ7u1mNSHHjL2gps6dVTuBbfuwLpuYQ6M/eFeP/062YnZ3FFwByW5JZTkljAmcXQOMxVXp6G1nYPnnRy80NRvmPvtF+cwM89BUVYSZtPI//JEiCuRQCeEEEJgzINzf7QT58svg9a4XnkF1yuvyF5wPWitOdZwjGp3da/3KxTvPPTOqOilFNfOFwjxaU2zEeDON3HwgpNzDW0AmE0Kq1nhD14+iiw/1cZn547cYcxCXAsJdEIIIUYtHQjQtv8ALdu20rp1W2QeHAAmE0l33kn+b/43pgTZMPiS+xIbKzZSerqUcld5n+1y7DkS5sRlql2ezvB23snhShftgRAAY5LjmVeYxmPzC5lbmMasfAdbjtZ0m0MHxhDLH6y4KVpPQYhhSwKdEEKIUSXk9eLeuZOWd7bS+u67BJ1OoxeuuBh/bS0EAuGGIdwffUSopWXUBjpPwMP289spLS+lrLqMkA5xc9bNPL/weRSKX+35VbdhlwnmBNbMWxPFisVw4PUHOVzpioS3g+ed1DQbvydxFhOz8h18acE45hamMbcwlVxHwmVfAnSsZjmQVS6FGO0k0AkhhBjxgi4Xre+9R8vWbbTu2IH2eDAlJ5N0550kL1tG0m2LufTrv6dt795uj9OhEHW/e4HcnzwfpcqHntaaA7UHKC0vZcvZLbj9bvLseTw560lWTVxFYUphpK3NYutzlUsxOmitOdfQxsELneHteHUzgZAxXLIwPZGSonTmjk1lbmEa03JTiLMMbM7pg3PzJcAJMQAS6IQQQoxI/poaWrZto2XrVtr27IVgEMuYMaR+9kGSli7FPn9+t7lwno8/Br+/x0H8eA4eHOLKo+NCywXeLH+T0vJSKlsrSbQksnzcclZPWs0t2bf0ujfc/UX3S4AbZVq8fj654IrMezt4vommNuPvxh5nZs7YVJ66o4i5Y9O4uTCVzKT4KFcsxMgngU4IIcSIoLXGV15Oy9ZttGzbhvfwYQDiiorI+NrXSF62lISZM/tckbJow+tDWe6w0Opr5e1zb/PG6Tc4UHsAhaIkt4Rv3/xtlhYuJdGaGO0SRRQFQ5rTta2dQycvNHGqtpWOHa8mj0li+fTsyNDJyWOSZdVJIaJAAp0QQoiYpUMhvIcO0bJ1Ky1bt+E7exaAhDmzyXrmGZKXLSW+qCi6RQ4zwVCQsuoySstL2X5+O96gl/Ep41kzbw2fKfoMOfacaJcooqShtZ2PLzgj4e2TCy5a2405pamJVuaOTeUzs/OYW5jK7IJUHLbRu9qrEMOJBDohhBAxRft8uHfvMVam3LadQF0dWCzY588n/ct/RtLdd2PNzo52mcNOubOcN8rfYGP5Rmo9taTEpbB60mpWTVzFrMxZsjLlKOMPhvi0uiUy9+3A+aZu2wZMzUnmwbl5zCtMY25hGuMzEuV3RIhhSgKdEEKIYS/Y6sa940NjZcr33yfU2opKTCTp9ttJXraUpDvuwJySEu0yh50mbxN/qvgTpeWlHGs4hlmZuT3/dp6d9Cx3FNxBnDku2iWKIVLj8nab93boYue2AVnJ8cwrTOXR+YXMHZvKrAIHiXHyEVGMcIdegm0/BddFcBTA0udh9sPRruqayF+rEEKIYSnQ0EDL9u20bt2Ge9cutM+HOT2d5JUrSF66FPvChaN2O4H++IN+Pqj8gNLTpXxQ+QGBUICp6VP5y1v/kvsm3EeGLSPaJYobzOsPcqTSFRk6efC8k2pXeNsAs4mZ+Sk8sWAccwuNlSfzetk2QIgR7dBL8OZ3we8xrrsuGNchJkOdBDohhBDDhu/CBWNRk61b8Rw4AFpjzc8n7dFHSV6+DNvcuSizOdplDjtaa441HOON8jfYVLEJZ7uTjIQMHp/6OA9MfICb0mUz5pFKa835xrbOTbsvODlW1bltwNh0G7eOT4+Et2m5ycRb5G9IjHLbftoZ5jr4Pcbtoy3QKaVWAusAM/AHrfUv+2j3EPAycKvWet9gzimEEGLk0FrTfvx4JMS1nzwJQPzUqWR++9vGoiY33SS9B32obavlrTNvUXq6lHJXOXGmOO4qvItVE1exKG8RFpN8bzvStHj9HLrYZdPuC04a3T4AEuPMzClI5RtLiphbmMbNY1PJSpZtA8Qo5vdC4xloOAUNp6H+tHHZdaH39q6LQ1vfdXLN/9IrpczAPwHLgYvAXqVUqdb6WI92ycB3gd2DKVQIIcTIoAMB2g4coGXrVlq3bsNfVQUmE4nz5jHm2R+SvGwZcQUF0S5z2PIEPLx7/l1Ky0vZVb2LkA5xc9bN/HjBj1kxfgWOeEe0SxTXSSikOV3XZduA805O1rZEtg2YNCaJpVPHRLYNmJIt2waIUUhraK4yglp9R3AL/9d5HtCdbZNzIWMSxNnB5778WI7Y/H/PYL66mw+c1lqfAVBKrQdWA8d6tPsZ8HfAXwziXEIIIWJYyOvFvXMnLVu30bp9O0GnExUXh33xYjK//S2S7roLS3p6tMsctrTWHKg9QGl5KW+ffZtWfyu59ly+PuvrrJq4inEp46JdohiADQcr+fWWE1Q5PeSl2vjBipt4cG5+5P4mty+8bYAxdPLj805awtsGOGxW5hamct+sXOYWpjKnIBVHomwbIEaR9pZwUCvvEt7C1/1tne2sdsiYCAXFMOdRyJxsXM+YBPHJRpuec+gArDZjYZQYNJhAlw907a+8CJR0baCUmguM1Vq/pZTqM9Appb4BfAOgsLBwECUJIYQYLoIuF63vv2+sTLljB9rjwZScTNKdd5K8bBlJty3GZLdHu8xh7ULLBd4qf4vS8lIutl7EZrGxfNxyVk9cTXFOMSbV+ybpYvjZcLCS5147jMcfBKDS6eGHrx5ix6k6QhoOXnBSUW/0GJgUTM1JYdXNeZHet6JMuww9FiNfMADOc1162cKBrf4UtNZ0tlMmSC00Qtq42yBzEmRMNsJbci5c6W+lY56crHJJb69UpE9TKWUCfgt85UoH0lr/Hvg9QHFxsb5CcyGEEMOUv6aGlm3GfLi2vfsgEMAyZgypn32QpKVLsd96KypOlsrvT6uvlbfPvc0bp9/gQO0BFIr5ufP51s3fYmnhUhKtidEuUVyDv9vyaSTMdWgPhHjlQCWZSca2AQ8Xj2VuYSqz8h3Y42X+oxihtIa2hs5hkQ2nOue2NVZAyN/Z1pZmBLVJS43wljHJCG1pE8A6yFWOZz8cswGup8H8a3ERGNvlegFQ1eV6MjATeC/8jVIOUKqUWiULowghxMjRXl5Oyztbadm2De/hwwDEFRWR8dWvkrx8GQkzZ6JM0pPUn2AoSFl1GaXlpWw/vx1v0Mv4lPF8d+53+UzRZ8hNyo12ieIanK138+GpOt4/WU+V09trGwXs/V9LpfdNjDxdFyTpOVTS6+xsZ7JCehFkToGb7gsPkZxshDe7bLMyEIMJdHuByUqpCUAl8AjwWMedWmsXkNlxXSn1HvAXEuaEECK26VAI76FD4Z64bfgqKgBImDObrGeeMVamLCqKcpWxodxZzhvlb7CxfCO1nlpS4lJYPWk1qyauYlbmLPmQH2OavX52nm7gw1N1fHiqnvONxryesek2EuPMtPmClz0mL9Um77OIXdeyIMmMz3aGtsxJ4CgEs/RID8Y1v3pa64BS6jvAFoxtC/5Na31UKfVTYJ/WuvR6FSmEECK6tM+He89eWra+Q+u27QTq6sBiwT5/Pul/9iWS7r4ba3Z2tMuMCU3eJjZVbKK0vJSjDUcxKzO35d/GDyf+kDvH3kmcWYakxopgSHPoopMPTtbz4ak6Dl5wEgxp7HFmFk7M5Ou3T2DJ5CzGZSTyxsdV3ebQAdisZn6wQvYIFDEgsiDJ6cvnt13tgiTiulNaD68pa8XFxXrfPunEE0KIaAu2unHv+NBYmfL99wm1tKASE0m6/XaSly0l6Y47MKekRLvMmOAP+vmg8gNKT5fyQeUHBEIBpqZP5YGiB7iv6D4ybZlXPogYFiqdHj48afTA7Thdj8vjRymYle9gyeQsbp+cybxxaVjNlw8zvtIql0JE1VUvSBIeFnm1C5KIAVFK7ddaFw+krfRvCiGEiAg0NNCyfTutW7fh3rUL7fNhTksj+Z7lJC9bhn3hQkwJg5yIPkporTnWeIzS06VsqthEU3sTGQkZPDb1MVZNXMVN6dIzEwvafAF2n2nk/ZN1fHiqjvI6YyXKnJQE7pmezZIpWSyelEm6/co9qw/OzZcAJ26cQy9dedXG67EgSXoRWGTD+uFEAp0QQoxyvgsXaNlqrEzpOXAAtMaan0/ao4+SvGwptnnzUGZztMuMGbVttbx15i3eLH+T087TWE1W7hp7F6snrWZR3iIsJvlf73AWCmmOVTfz4al6PjhZx/5zTfiCIRKsJkomZPDo/EKWTMli8pgkmfsmho+e+6q5LkDp/4DK/WDP6ntBEnOcLEgyAsiQSyGEGOH8tbVUPvN9Cn77GyxZWWitaf/008jKlO0nTgAQP3UqycuWGYua3HSTfFi9Cp6Ah3fPv0tpeSm7qncR0iHmZM1h1cRVrBi/Ake8I9olin7Utnj5MDwPbsfpeupbfQBMzUlmyZQslkzOonh8GglW+WJDRIHWxjy1tkbwNIKnqcflJtj/f7vPZeupY0GSzHBYkwVJhj0ZcimEECKi/ncv4Nm/n+q1a4krKKBl6zb8lZVgMpE4bx5jnv0hycuWEVdQEO1SY4rWmgO1BygtL+Xts2/T6m8l157L12Z+jVUTVzHeMT7aJYo+eP1B9p1tCm8pUMenNS0AZNjjuH1yJreH58KNSZHhxeI6C/j6CGU9Lzd1vz3o6/uYVns/YU7BcxdkQZIRTgKdEEKMACGvl0B9A8GGegINDQTq6wk2NOC7cBHXhg2gNa3btoPVStJtt5H5radJuusuLOnp0S495lxsucib5W9SWl7KxdaL2Cw2lo9bzuqJqynOKcakZM+94UZrzena1vA8uHp2VzTg9YewmhXF49L5y5U3sWRyFtNzUzCZpGdaDEAoCF5Xl/DVOIDLTeBr7fuY5jiwpRtz1xLTjaGQ+bcYl7ve3u1ymjGf7bczjWGWPTkKJMyNAhLohBBimAq53eFw1kCgwQhokcv1DcZ94csht7v3g1itEAoZl81mHA+uJu9nPxu6JxFjNp7ZyLoD66hx15Bjz2HNvDXcX3Q/rb5W3j73Nm+cfoMDtQdQKObnzOfpm59mWeEyEq2J0S5d9NDk9rHjtDEP7sNT9dQ0Gxt7F2XZeeTWQpZMyaRkQgb2ePkoNGwNZJGPwdLaCFkdPWGX9Y71EdC8LrrtsdaVMkFCamf4Ss6F7Bldglha7wEtzn7tK0Qufb77HDoAq824XYx4ModOCCGGiNaaUEtLj5603gNaoLER7fH0ehxzairmzAwsGZlYMjI6L2dmYM7ovKxDIc7cdz+6vT3yWBUfz6St72DJyhqqpx0zNp7ZyNqda/EGvZHb4kxxTEufxommE3iDXsaljGPVxFU8UPQAuUm5UaxW9OQPhjhwrslYzORUHYcrXWgNKQkWbpucyZLJWdw2OZOCNAnfMaHnIh9gBJQH/qHvUOf3DmA4Y9PlQxu7ruzYU3wK2FKN0NXRI9bn5XBAi3eAKQo99UMRgMWQuZo5dBLohBBiELTWBJ3O7r1nvQa1BoINDWhfL/MgTCbM6elYMjKMgBb+rxHQjHBm3J6JJT0NZbUOqLbqtX+N89VXwd/lw4rVSupDD5H7E/nWtqd7XrmHanf1ZbcrFF+Y8gVWTVrF7MzZsljMMKG15lxDGx+cquODk/XsKq/H7QtiNinmjk015sFNyWROQSpmGUYZe/oaQhifDDMf6tJb1iWg9bcoiCWh9/B1pYBmHti/t0Jcb7IoihBCDIIOBgk6nZf1pAUb6sNBrXtPGoHA5QexWLCkp0d6z+InTeo9oGVmYE5NvSHbAng+/rh7mAPw+/EcPHjdzxWL/EE/p52nOdpwlKMNR3sNcx1+vPDHQ1iZ6Euz18/O0w18eKqOD07VcaHR6L0Zm27jwbn53D45i4UTM3DY5EN4TPG1Qf1J46fuU6g70XuYA2hvgeNvdg5VTB0LubOvHNCstqF9TkIMIQl0QoiY03MZ/oHQgQCBxsaB9aQ1NnbOO+tCWa2YM41hjtasMSRMm9brUEdzRgZmhwMVjSE3XRRteD2q5x9O/CE/Z5xnONZwzAhw9Uc50XQCf3ioVbI1mThTHL7Q5T2oOfacoS5XhAVDmkMXnXwQ3lLg4AUnwZDGHmdm4cRMnry9iCWTsxiXkSg9p7HA64K6k1B/ojO41Z0A53ki89FMFkifCBYbBHoZdu4YC987MqRlCzHcSaATQsScjmX46/7xH8l8+unLes96uxx0Oo3J7z2ohITIPDRrfj622bO7zUnr2pNmSk6WD40xIBAKUOGqiAS3Y43HONF4gvagMZcwyZrE9IzpPD7tcWZkzGBGxgwKkgv4U8WfLptDl2BOYM28NdF6KqNSpdPDhyeNHriPTtD0/L4AACAASURBVDfg8vhRCmblO3j6joncPjmTef8/e/cdHlWZ9nH8e6alZya9h5aQ0EIHFUSlKlLsfV93XVf3VRfXjmUVKyiuLrqW1VXX193VtSBFQBRYxUqHAKGFACG9zqRn2nn/OMkkQxJAAmncn+vKlWTOmZnnMBDml+d57rtXCEa9VBPtsqpLtcBW0hDYivdqQa4yr+kcvY/WzDp+FAy/CSJSIDxFq+xoMLW9h06KfAjRguyhE0J0G46iIso/+g+lb7zRajhrpPP398yktTZ7Zmg4pg8LRxcgv9nvzlxuF0cqjniWTTbOvNU2/Gbf3+DPgLABDAobxMCwgQwKG0RicGKbrQXaqnIpzpwau5Ofs0o9s3AHi7WKrdHBvpyfHM6E/hGMSwonNMDUySMVXlQVKgsagtt+7xm3mpKm84wBWljzfKRqQS6kN+hOsNRcinyIs5gURRFC9Bjumhoq167FtnQZ1T/+6L0UUqfDb+RIwn59c8MsmxbUdH6yV6IncqtuT3jLKM1gd8lu9pTt8YQ3P4MfqaGpTeEtfBC9gnqhP9GbRtGh3G6VjPwKrRrl/mI2HynD4VLxNeoY2yfME+KSIwPlly1dgdut7Wcr3nfMUsn9UG9rOs/XDBEDIKK/FtoaZ9yC4zqn4qMQ3ZwURRFCdGuq203Nxo3Yliyl8quvcNfUYIyNJeTGG7D+5+OmSpFuN3Xp6filpUkZ/h5GVVWOVh71WjaZUZpBtUObvfHR+5AamsplSZd5lk32MfeR8NZFFVXU8d0BbQbu+8wSSqq0f8Op0UH8ZlwfJiRHMKp3CL5Gef06jcsJ5YdbLpUsOeBdPTIgUgtraVd7B7fAyFPvoSaEaBcJdEKILqM+MxPb0mXYli/HWVCALjCQoOmXYJ41C/9Royh46mmOXVWgut0Uv/6GlOHvxlRVJacqx1OwJKMkg4yyDCrtlYDWCy4lNIUZfWd4Zt/6Wfph0Ml/YZ1tybZcFq7eR561lliLHw9MS+Gy4XHUOVxsPlze0FKgmL0F2msZFmDi/ORwraVAcjiRwb6dfAVnIWc9lB5suVSyNBNczYoCBcdrs20jxzWFtogUrWKkEKJLkf8NhRCdyllaSsWKFdiWLKUuIwP0egLHj8f84AMETpyIzrfpDZ+U4e/+VFUlvzrfa9nk7tLdVNgrADDoDKSEpHBx74u1mbfwQfSz9MOokzL0Xc2Sbbk8vHgntQ4XoBUzeeDTHbzx7UEOl1RT73Rj1CuM6hXKQxencn5yOANjgtFJT7iO0dgKwLNUsmHGrewQqK6GkxRtL1tECiRPadjflgLhyeAb3JmjF0L8AhLohBAdzl1XR9W6dViXLqX6+x/A5cJ30CCiHnmY4OnTMYSHt3q/PS/d2moVwnnn3Urfjhq8OGmqqlJYU+i9bLIkg/L6cgAMioHkkGSm9JrCoHBt5i3ZkoxJL8UvurrqeifPrMjwhLlGDpfKwaIqbjqnFxf0j2Bs31D8TfJW44xqbAVw7FJJ61FatAKIHACDLm8qTBKeLP3ZhOgB5KesEKJDqG43NZs3Y1u6lMrVX+GuqsIQHU3YLbdgnj0Ln6SkEz7Gy1te9gpzAHWuOl7c/CIjo0Zi9jHjZ5A3J52lqKbIq89bRmkGpXWlAOgVPf0s/bgw4ULPzFtySDI+ep9OHrU4kRq7k4y8Cnbm2tiZYyM918bB4qo2C8263CrzZg3q2EGeDRpbARy7VLIyv+kcTyuA0TD8Vy1bAQgheiQJdEKIM6o+6xC2ZUupWLYcR14eOn9/gqZNwzx7Fv5jxrTafLvWWUuWNYsD1gMcKD9ApjWTA+UHKK4tbvU5SmpLmPLpFEArlmH2MWP2MWPxsWDxsRBsCvZ83fxY49dmH7Ms6fuFSmpLvPa87S7d7Xl9dIqOvua+jI8b76k2mRKSgq9B9kt1dXUOFxn5FVpwy7GxK9fGgaJK3A3hLSLIh6HxZmakxfDBT0corW7ZiD3WIr9UadOJyvA3bwVw7FLJmtKm8xpbAfS98Je3AhBC9DgS6IQQp52zvJyKFSuxLVtGXXo66HQEnHceEffcQ9Ckiej8/bXz3E6yrYdbBLejlUdRG5YK+eh96Gfpx7mx5/LN0W88e62aC/EJYc6IOdjqbdjqbVjrrZ7PWdYsbHbta6fb2eaYA4wBTSHPZG4z/DUPhkGmoDb7mfUkZXVlnv1ujSGusKYQAAWFPuY+nBNzDoPCtWqT/UP642/07+RRixOpc7jYW1DZMPNmJT3HxoGiKlwN6S080MSQODPTBkczJM5MWryZqGZFTHqHBXjtoQPwM+p5YFpKh19Lt3Bso2zbUVh6J+xfDUbfph5u9c1+xjW2Aki9VFoBCCHaJH3ohBCnhbu+nqr/foNt2TKq1q8HpxOf1FTMs2cTPH06pYFu9pfv94S2A+UHyLJl4XBrRU50io7EoESSQ5JJtiSTHJJMkiWJhKAETyn6FVkr2thDN++EzZ9VVaXGWeMJetZ6KxX1FZ6vjw2DjSGwor7CEy6PpVN0BJuCvYOfyTsEthYG/Qx+Xba/lq3e1qJgSX5105Ku3sG9PQ26B4UPIjU0lQBjQCeOWJwMu9PNvoJK0nOt7MrVZt/2FVTibAhvIf5GhsRbSIszMyTezJA4MzFm3xP+PW2ryqVoxmmHogz44DKoLW/9nMZWAI2zbdIKQIiznjQWF0J0CFVVqd26FdvSZVR8+SXuigp0EeHUTRpD1jkJ7DRXkGnNJLM8k0pHped+Uf5RLYJbX0vfk9pPtSJrBYu2LqKguoDogGjuHnH3CcNce7jcLqocVW0Gv7Zuq3HWtPmYRp3RE/A8y0F9vWcHLT4Wgn28l4qeSrGQ4/15VdgryCjN8ApvuVW5nvsmBiV6gtvAsIGkhqYSZAr65X+IokM5XG72F1Z69rvtyrWxN78Su8sNgNnPSFq8mcFxZk+Ai7N03V8ydCuOWijMgPxtkL9D+yjMALfjOHdSYJ61w4YohOgeJNAJIc4o+5EjlHz+GdZlS1HyinCaDBwYGsqaAU6+j7GhNpQlDzIFeUKbJ7yFJBFs6vnlsB0uhzbLV2f1zPa1mAVsZbbQ7m65J6mRn8Gv5VLQ48wIbirYxMJNC71mNA2KgYFhA7HWW8muzPbcHh8Y79nvNihsEAPCBpwVr1N353S5OVBU5VWwZE9+BXanFt6CfA0MaQhtaXEWhsSZSQiV8HZa1FdB4a6m4Ja3Xdvr1tgSwC8EYoZBzFDtY/Uj3gVMGpkT4J5dHTt2IUSX90sCneyhE0Icl9PtJLsym4PZ26le9RXmb3YQnWXFDezqrbB+ho7tA03ERWizbvc2m3WL9I88a984GvVGwv3CCfdrvQVDa1RVpdZZS4W9aSno8ZaG7qve51ke6lbdJ/UcTtXJ7tLdTEycyOXJl3uWT5p9zKd6qaKDuNwqB4urSM/R9rztzLWRkV9BnUN77QN9DAyOC+bmc3t5lk8mhvpL37fToc4G+elN4S1/h1ZpsnE5dkCEFt5SLoHYhhBnTvBeLul2eu+hA61lwKTHO/RShBA9j8zQCSGApp5hzfe5HSrZj3lzJufudDAyU8XghvwoE9nn9cE9+TwSkoaTbEn22ucmOp5bdVPlqMJW1zD71zAj+PB3D7d6voJC+s3pHTxK8Uu43SpZJdXszLU2BDgbu/MqPAVI/E16Bsc27XcbEm+mT1iAhLfToaasWXDbrn0uy2o6HhTbFNpihmpBLij65Pa6najKpRBCNJAll0KI47LV27TCJNYDZJZnej5XOipBVUnOhYv3+TJqVz1+NU6clkAM0y4i9uobCR6UdtbOunU3Uz+d6lXQpFFMQAxfXfVVJ4xItMbtVjlcWu21bHJ3ro1quxbefI06BsWaPZUm0+LN9AkPRC/hrf2qihtCW0Nwy9sBtqalyFgSm0JbzDCISdMKlQghxBkmSy6FEEBDPzdbltYSoFlwK6ot8pzTuM/t6sDzGbatgsj1e9DlFqL4ugiapPWLCzjvPBSD/Ljobu4ecXerVUHvHnF3J47q7KaqKtllNdqsW0OA25Vro7Jea6nhY9AxMDaYK0fGNwQ4C/0iAjDopUR9u6iqtn+tca9b4wxcZV7TOaH9IH4UjP5t0+ybf2jnjVkIIU6SvEMTogdo3OfWPLQdsB4guyLbq59bX3Nfzok9h2SLVpykny4a3/VbsP1zObVblgHgP3Ys5jvmEDRtKvrAwM68LNFOjdUsO7IqqGiiqio55bXsbGgTsDPXys4cGxV1Wngz6XUMiAli9vBY0uIsDI4zkxwViFHCW/uoKlizWy6brC5uOEHRmnD3Ht+0dDJ6iNbzTQghuiFZcilEN9K4z+3Y5ZJZ1ixPdcTm/dySLEmez4lBieh1elS7narvv8e2dBlV//0vqt2OqV8/zLNmYZ45A2NsbCdfpRDdj6qq5NnqPA26d+ZqH9YarVy9Ua+QEh3EkDgLaQ373vpHBWEySHhrF7cbyg81hbbGj8Z+b4oeIgd473eLGgQ+8ssqIUTXJksuhehG2uoT1rjPzdOIu/k+twaR/pEkhyRzTsw5Tf3czH3xNfh6PYeqqtTt2oVtyVIqVq7EVV6OPiQEy7XXYp41C9/Bg2RfnBCcXKNsVVUpqKhjZ0NwS29YNllarf1SxaBT6B8VxMWDorVeb/FmUqKD8DFI4aB2cbugNNO7TUBBOtRXaMd1RogaCANmNQtvA7VKkkII0YPJDJ0QnWhF1ooWe5x0io4AQ4BXcAsyBmm93I6ZdTtRqXlHbi625cuxLV2G/dAhFJOJwEkTMc+aReD48ShG4xm7NiG6myXbcnl48U5PJUkAP6Oehy9JIdbi75l1S8+xUVJVD4Bep5AcGegpWDI4zsyAmGB8jRLe2sXlhJJ93vvdCnaCo1o7bvCFqMFacGtcNhkxAAymzh23EEKcJjJDJ0Q38fKWl73CHGgl6J2qk3tH3usJblH+USc9g+aqrKRy9WpsS5dRs2kTAP6jRhF6y28InjYNfbA0ixaiObdbxVrr4LmVe7zCHECtw8XjyzIA0CmQFBnIhP7hpMWZGRJvYWBMMH4mCW/t4qyHoj3e+90Kd4Oz4WejMUDb4zbiV01LJ8NTQC9vYYQQAiTQCdEpXG4Xnx34jMKawlaP1znr+M3g35z046lOJ9U//IBt6VIq165Dra/H1KsXEXfPIXjmTEzx8adr6EJ0eaqqUlHrpKS6nrJqO6VV9ZRW2ymtslNWbaekqvF2O6XV9ZTXOHC5j79a5ZPfn8vAmGACfOS/zXZx1GphzdMmYLsW5tzaXkN8grXANvrWhjYBQyGsH0ifSyGEaFO7/mdSFOViYBGgB/6uquqCY47/HrgTcAFVwG2qqma05zmF6O4ySjN45udn2FmyE5PO5Clm0lx0QPQJH0dVVeoyMrAtXUrFipW4SkvRm81YrrwS8+xZ+KZJvzjRM6iqSlW90xPAtM+thTMtvJVV23G2EdCCfA2EB/oQGmCiV5g/I3qFEBZgIizQxCtrD1DeUMSkuTiLH6N7S/n6Vh2vUXZ9FRTu8m4VULwX1IZZUL8QLbSde2fT0klLb9BJoRghhPglTjnQKYqiB14DpgA5wCZFUZYdE9j+rarqmw3nzwJeAi5ux3iF6LYq7ZX8ddtf+WjfR1h8LMw/fz6o8ORPT/6iPmGO/Hxsy7/Atmwp9syDKEYjgRdeiPmy2QSefz6KSfaQiK5NVVVq7K4Wgaykup6yxmDWLJyVVtmxu9ytPlagj4HQhkAWZ/ElLc5MaKCJsACTJ7iFBZoIC/AhJMB43MIkIf6mVvfQPTAt5bT/GfQI6R/D8jnarBuA7SgsuQM2/h3qyqHkADS0TSEgQgtvqdOblk2aE0B+6SSEEO3Wnhm6MUCmqqpZAIqifATMBjyBTlXVimbnB+D5yS7E2UNVVVYdWsXCzQsprS3l2pRr+cOIPxBs0vayKYpywj5hrqpqKr/+GtvSpdRs2ACqit/w4UTPm0fwxdPQWyydcWlCeNQ5XG3OlpU0zKw1X+ZY52g9oPkZ9Q0BzERUsC8DYoI934cF+HjCWVigidAA02ktPtJYzfJEVS7Pem63NtO26sGmMOc55oDczdB/Ggy+smnZZFC0hDchhDhDTrnKpaIoVwEXq6p6a8P3vwLGqqp61zHn3QncC5iAiaqqHmjlsW4DbgNITEwceeTIkVMakxBdzSHbIZ7d8Cwb8jcwMGwgj5/zOIPCB53UfVWnk+qfftb2xa1Zg1pXhzEhAfPs2ZhnzcSUmHiGRy96gpMpw9+aeqerzXBW1mzZY+MSyBq7q9XHMRl0hAeYCPOaLWv6PjzQRGiAj2fZo79J9qh1OXU2yNkMRzdCzkbt6/qK49xBgXnWDhueEEL0RB1V5bK1X7W1SIeqqr4GvKYoyg3AY8DNrZzzFvAWaG0L2jEmIbqEOmcdb+98m/d2vYev3pdHxz7K1f2vRt/Gxn5HURG5995H/Msv4SwtxbZ0GbYvluMqLkEXHKyFuNmz8Rs+TPbFiZN2bBn+XGstD32Wzr6CSlJjglqGs2YzbJX1zlYf06hXtGDWMEvWO8y/ZThrFtoCTHr5O9udqKrW6+3oRji6AXI2aUVLUAFFa8o9+EpIGAtrn4TK/JaPYZYiTEII0ZHaE+hygIRm38cDecc5/yPgjXY8nxDdwvqc9Ty34Tlyq3KZ0XcG9426j3C/8OPep+jPL1G7eTMHZ8zEbbOB0UjghAlav7iLLkQn++IEWnn9ynonFbUObA0f1pqmr5s+7NhqHWw8VIbD5f07snqnmze+Pej5Xq9rDGjaDNmQEEvD/rOW4Sw0wESwr0ECWk9SXwV5WxsC3EYtwNWWacd8zRA/GgZeBgljIG4k+DZre6LTe++hA62J96THO/YahBDiLNeeQLcJSFYUpQ+QC1wH3ND8BEVRkpstsbwUaLHcUoieoqC6gOc3Ps+a7DX0MffhnanvMCZmTJvnu+12qtb9l7IPP6R2wwbttooKwu/5IyHXXIMhJKSjhi46kKqqVNtdWvCqcWCttZ9EQNM+KmodHK+6vlGvYPYzYfYzYPYztghzjRTg63svIDzQRLCvEZ1OAtpZQVWh/LAW2o5u0AJc4e6mqpPhKVrRkoSxED8Gwvsfv+JkYzXLtqpcCiGE6BCnHOhUVXUqinIXsBqtbcG7qqruVhTlKWCzqqrLgLsURZkMOIByWlluKUR353A7+FfGv3h9x+uoqsrdI+7m5oE3Y9QbWz2/bu9erIsXU7FsOS6rFcXPT3vT5HaDwYCzoFDCXBenqip1DrcWwGrt2I4TwhoDWvPQ1lZJfdBmzMx+Rs9HiL+J3mEBmP2MWPy124Ibjln8jJj9m871M3ovbxy3YB251toWzxFr8SMpMvCM/NmILsRRq7ULyNnYNANXXaQdMwVqM27n36sFuLiR4H8KrRnSrpEAJ4QQneyUi6KcKaNGjVI3b97c2cMQ4qRsLdzK0z8/TaY1kwviL2DumLnEB7XcP+KyWrGtWIHts8XUZWRorQYmTSJo0kTyH/sTan2951zFx4ekNV9jiIjoyEvpNk61yEdr6hyuk54d047bsdVqSx7bKqMPWjG/YN+mANYYwizNglpjQAv2+t50WvecHbuHDrQqkvOvGCKVG3siW27TvrejGyA/valhd0gfLbgljNY+Rw6UZt1CCNGFdVRRFCHOWmV1Zby85WWWZC4hJiCGRRct4qKEi7zeiKsul1alcvFnVK5Zi2q34zNgAFGPPkrwjEsxhISQP+9JVLd3MFDdbopff4OYJ2QfyrFaK/Ixd3E6lfUOzusXjrXGcYKAZvf6vq3S+Y2CfA1eASwlOuiYGTJTqwEtyMfQJZYxShn+Hsxph4KdDQGuYfatIlc7ZvCF2BFaw+6EMdryyUD5BZEQQvRUEuiE+AXcqpvFBxbzl61/odpezS2Db+H2tNvxN/p7zrFnZ2P9/HNsS5bizM9HZzZjueYaLFdcju/AgV6PV7t9Ozgc3k/icFC7bVtHXE63oqoqz63c4zXbBFDncPOnJbvbvF+ASd80Q+ZvpE94gNeMmNcMWbOvg3wNGPTH2T/UTVw2PE4CXE9QVeRdeTJvGzjrtGPmhIbZt4YZuKghYJBCSkIIcbaQQCfESdpbtpenf36a9OJ0RkaN5LGxj5EUkgSAu6aGiq++wrb4c2o2bgRFIWD8eKIefIDAiRPR+fi0+ph9l3zekZfQ7didbjYcKmVNRiFr9hRRVFnf5rl/uXaY136yxg9jDwhl4izjckLR7qZ9b0c3gLWhP6vepDXqHn2rVoEyYQwEx3bueIUQQnQqCXRCnECVvYrXtr/Gv/f+G4uPhWfHP8vMvjMBbYbN+tliKlauxF1djTExkYg/3o159myMMTGdPPLuqbzazjf7i1iTUcS3+4upqnfia9QxPimc6non1lpHi/vEWfxkFkp0XzVl3pUnc7eCo1o7FhilhbYxv9OWTsYMBaNv545XCCFElyKBTog2qKrK6iOrWbhxIcW1xVzd/2rmjJhDQIWdsnffxfrZYuxZWSh+fgRPm4blyivwGzVKenSdgqziKtbs0WbhNh8uw61CRJAPM4fGMCk1inFJ4fiZ9G0W+XhgWkonjl6IX8DthuK93pUnSxs6+ih6iB4Cw29saB0wGiyJWpUdIYQQog0S6IRoxZGKIzy34Tl+zPuRAaEDePn8hfTZXYb1jw+Tt349uFz4DR9OzDNPE3TxJegDAzp7yN2K0+Vma7a1IcQVklWszUakRgdx50VJTB4QxZA4c4vCIlLkQ3Q7dTbI2dw0A5ezBept2jG/UC24Dbte+xw7HEzys0QIIcQvI20LhGim3lXPOzvf4Z2d72DSm7g/7FrO21ZH5fIvcJWWoo8Ix3LZZZgvvxyfvn07e7jdSmWdg/X7S1i7p5B1+4qw1jgw6hXO6RvG5AFRTBoQSXyI/4kfSIiuSlWh9KB35cmiPYAKKBA1qGHf21htGWVoX5l9E0II0SppWyDEKfgh9wee3fAsJcXZ3FE0kAk7nDh3/Q2rwUDQRRdhvuJyAs8/H8Ug/2xOVk55DWv3FLFmTyE/Z5XicKlY/I1MTIlk0oAoJvQPJ8i39QbsQnR59VWQt7Vp6WTOJqgt0475mLWKkwMv08Jb3EjwDe7c8QohhOiR5J2pOOsVVhfy/IYF5H33FTfu8Wd4hg7FvhN9cjKhcx/CPGsWhtDQzh5mt+B2q+zMtbFmTyFfZxSyt6ASgL4RAfxmXB8mD4hiRKKlR7QDED1U+sew9imw5YA5HiY9DmnXaLNv1iPelScLd4PasJ8zvD+kTtcKlySM1b7Xyd9zIYQQZ54suRRnLafbyaffvkHWh+8wfoedCJuKLiiI4BmXYrniSnwHD5ICJyeh1u7ih8wS1u7VipoUV9ajU2BU71AmD9Bm4vpFBHb2MIU4sfSPYfkccNQ23aYzakslK/Kguki7zRQIcSMaCpeMgfhR4C+/9BFCCHH6yJJLIY7DXVfHrs/e4chH7zHkQDVpChhGDyfqmhsJmjwJna+UBD+Roso61jUspfw+s4Q6h5tAHwMX9I9g8sBILuwfSUiANDbuEtqacequ3C6tobajTvvc+OGoA2dty2OOWnDWa8davU+zr3M2gst+zPM5oHAnDL5KWzoZPwYiB4Je/vsUQgjRNcj/SOKsoKoqdbt2UfTxR1i/WI6p1kGIRU/Vr6Yz/OZ7MMXHd/YQuzRVVdlbUMnaPYV8vaeIHUetgNb/7dpRCUweGMXYPmGYDLLErEs5dsbJdlT7Htof6lS1jWDULEA56xu+PzZ01Z9E0Go875j7uFv2ITx5Chj9wODb8NkHDH5aXzeDb8sw18jthiveasfzCiGEEGeOBDrRoznLyrAtW6b1jDtwALsBNqboMMy8hGuuf4oAH1kK2Ba7082GQ6WsydCWUuZatVAwNMHC/VP7M2lAFKnRQbIstStb+5T38kHQvl/5gLaE8ESB7HhBy1nXvrEZfJsClcGnKWgZfMHkD/5hLW9vDF5tBTKv81p5XL3x+FUlXx6shd5jmeUXPkIIIbouCXSix1GdTqq++w7b4sVU/vcbcDrJSwzgi4t1VE0YygMXzaN/SP/OHmaXVF5t55v9RazJKOLb/cVU1TvxNeoYnxTOHyYmMTE1kshgWZLaJbndYD0MBbugYCcU7mo9nADUWWHNE9rXOmMrIajZ177BrQen5oGq1eDV/PZj7q/36ZoFQyY93nIPndFPu10IIYTooiTQiR6jPisL2+LFWJcuxVVcgi40hKwpqbwRvw9bnB/3jnyU2Umz0Sld8I1kJ8oqrmpo8F3E5sNluFWICPJhRloMkwdEMS4pHD+TvrOHKZqz12j9zQp3auGtYJdWcdGuVRVF0UFYMhj9wVHT8v7BsXDXZi1c6eS19WhchtqT9hwKIYTo8STQiW7NVVVN5ZersH62mNpt20CvJ/CCCRw+vx9PKSsoqN/LlclX8scRf8Tia+ns4XYJTpebrdnWhhBXSFZxNQCp0UHceVESkwZEkRZnRqeTpZSdTlWhqrAhtO1smnkrzQTVrZ1jCoLowTDseogarH0dOVCbWWqtaqPRDyY/CaaAzrmmri7tGglwQgghuhUJdKLbUVWV2s2bsX62mIrVq1FrazH160fkAw9QddFInst6k+9z/0FKSAoLJ77EsMhhnT3kTldZ5+C7AyWsyShk3b4irDUOjHqFc/qGcfO5vZk0IJL4EP/OHubZzeWAkgMNoW1n09LJmpKmcyyJEDUEBl0O0UO0AGfp1fbyRZlxEkIIIXo8CXSi23AUFGBbsgTr4s9xZGejCwjAPGMGliuvQDd4AO/tfo+///hb9IqeB0c/yPWp12PQnb1/xXPKa1jb0Frg56xSHC4Vi7+RiSlab7gJ/cMJ8jV29jDPTrVWbabNs1xyp7aEsrHKot4HIgdAysVagIseovVC8zuFWWaZcRJCCCF6tLP3xYgwewAAIABJREFU3a7oFtx2O1Vr12L9bDHVP/wAqor/mDFE3HkHQVOmoPP356e8n3h2+ZUcqTjC1F5TeXD0g0QFRHX20Duc262yM9fGmj2FfJ1RyN4CbT9V3/AAfjOuD5NSIxnZKwSDXvYQdpjWCpUU7AJbdtM5ARFaYBv7e+1z9BBt/5v0ORNCCCHESZB3DKJLqsvIwLr4cyqWL8dls2GIiSH8f3+P+fLLMSUkAFBUU8SL385j1eFVJAYl8ubkNxkXN66TR96xau0ufsgsYe1erahJcWU9OgVG9Q7lkempTBoQRb8Iac3QIU62UEnCGBh9S9PMW9DZ98sHIYQQQpw+EuhEl+EsL6fiixVYFy+mfs8eFJOJoMmTMV95BQHnnIOi16rxOd1O/rPvP7y67VUcLgd3DL2DW4bcgo/ep5OvoGMUVdaxrmEp5feZJdQ53AT6GLigfwSTB0ZyYf9IQgJMnT3Mnqu9hUqEEEIIIU4jCXSiU6kuF9U//oj1s8VUrV2L6nDgO2gQUY//CfOll6I3m73OTy9O55mfn2FP2R7Oiz2PR8Y+Qq/gXp00+o6hqip7CypZu6eQr/cUseOoFYA4ix/Xjkpg8sAoxvYJw2SQpZSn3S8qVHKFFtxOVKhECCGEEOI0kkAnOoX9yBGsiz/HtmQJzsJC9BYLluuvw3LFFfimprY431ZvY9HWRXy6/1Mi/CJ48YIXmdprKorS/UvrL9mWy8LV+8iz1hJr8eOBaSlMHxLDhkOlrN1TxNcZheRatbLzQxMs3DelP5MHRpEaHdQjrr/L8BQq2dUU4NoqVBKdpgW3Uy1UIoQQQghxmiiqqnb2GLyMGjVK3bx5c2cPQ5wB7upqKlZ/hW3xYmo2bwadjoDzx2O54koCL7oQnanlMkFVVVl2cBkvbXkJa72VG1Jv4M5hdxJo6hn7wpZsy+XhxTupdbg8t+kVMOgU6l0qvkYd45PCmTwgiompkUQG+3biaHuI5oVKmleabK1QSdRgKVQihBBCiA6nKMoWVVVHncy58u5EnBGOoiJy772PuJf+jCMnB+vixVSuXIW7pgZTr15E3Hsv5tmzMEa1XRAiszyTp39+mq1FWxkaMZS/TfkbqaEtZ++6q9Kqep76IsMrzAG4VPDR6/j7jcMZlxSOn0nfSSPsAbwKlTTOvB2nUEn0EG35pBQqEUIIIUQ3IYFOnBFFf36J2s2byZp+Ke6qKhR/f4IvuRjLFVfgN2LEcZcK1jhqeDP9TT7Y/QEBpgDmnTuPy5MvR6d03z1JLrfK/sJKthwpZ2t2OVuPlHO4tKbN82vtLiYPlFDRqvSPWzbKHnJ1Q6GSXVCQ3jTzJoVKhBBCCNHDSaATp5XqdFK0aBEVS5cC2jLLyLlzCbn6KnQBAce/r6qy7ug6FmxcQEF1AZcnXc4fR/6RUN/Qjhj6aWWrcbD1aDnbjpSzNdvK9qNWquqdAIQHmhieGMJ1YxL5+3dZlFTZW9w/1iIho1XpH8OyOeDU9hRiOwqf3w7L7wFHVdN5UqhECCGEEGcJCXTitKk/eJC8uQ9Tt3MnKIpW3t1gwH7kyAnDXE5lDvM3zmd9znqSLEm8f/H7jIga0UEjbx+3WyWrpEqbfTtiZUt2OZlFWrjQKZAaHcxlw2MZ2SuEEYkhJIb6e2Yoo4N9W+yh8zPqeWBaSqdcS5fhdkNFLpQegNKDWqXJ0gOQ9S2o3ktUtRk4FS55QQqVCCGEEOKsI4FOtJvqclH2fx9Q/PLLKL6+YDCAU5uNwuHAtngxEXf8L4aIiBb3tbvsvL/7fd5KfwtFUbh/1P3cMOAGjDpjB1/Fyauqd7LjqNVr+WRFnXa9Zj8jIxItXDYslhGJIaQlWAj0afuf2WXD4wBaVLlsvL3Hq6vQglpJZkN4y2z4OrNpFg7AFAhhSS3DXCNHDYy9vWPGLIQQQgjRhUigE+1iz84m75FHqN28hcCJE9EFB1OxYoXXOarbTfHrbxDzxONet2/I38AzPz/D4YrDTE6czENjHiI6ILojh39CqqpypLSGrdnlDQHOyr6CCtwNxWH7RwUyfUgMIxpm3/qGB6DT/bJWApcNj+vZAc7lgPIjWkgrPdAw25apfa4uajpP0UNIL61ISZ8JEJ6kfR2eDIFR2qzvy4O1ZZbHMsd33PUIIYQQQnQhEujEKVFVFet//kPhCwtR9HpiFszHPHs2hy6/AhwO75MdDmq3bfN8W1JbwoubX2RF1griA+N5bdJrTIif0MFX0Lpau4v0HCtbs7UZuG3Z5ZRWa3vcAn0MDE+0MGViMiN7hTAswYLZr+vOJHYoVYXqEu/A1hjayg+B29l0rn+YFtT6T9U+hyVpoS2kDxhatq7wMulxWD4HHM1m74x+2u1CCCGEEGchCXTiF3Pk55P/6GNU//gjAeedR8yzz2CMiQGg75LP27yfy+3i4/0f8+rWV6lz1XF72u3cOuRWfA2d01tNVVXybHUNe9+05ZMZeRU4G6bf+oQHcGFKJCN6WRjZK4TkyCD0v3D2rcdx1EJZVtOetuZLJetsTefpfSC0L0SmwoCZWmALS4awfuDfjiI3addon4+tctl4uxBCCCHEWUYai4uTpqoqtiVLKXz2WVS3m6gHH8Ry7TXHbUHQaHfJbp76+SkySjMYGzOWR8c+Sh9znw4YdZN6p4tduRVs8yyfLKewoh7QCpGkxZs9hUuGJ1oIC/Tp0PF1GW0VJCnJbFju2OxnRnCcNsPWOMsWlqwtlTQngE765wkhhBBCnAppLC5OO2dxMflPzKNq3Tr8Ro0kdv58TAkJrZ67ImsFi7YuoqC6gEj/SPoE92FDwQbC/MJ4YcILXNz74pMKge1VVFHnCW5bjpSzK7cCu0vrSRYf4sc5fcMYkRjCyF4hpEQHYdSfZSXtf2lBksSxEHaTNssWngyh/cAnsPPGL4QQQggh2hfoFEW5GFgE6IG/q6q64Jjj9wK3Ak6gGLhFVdUj7XlO0fEqvvySgnlP4q6pIXLuQ4T+z/+gtNHPa0XWCub9OI86Vx0AhTWFFNYUcm7Mufz5wj8TZAo6I2N0uNzsza9ky5Eyz/63XKsWSkwGHUPizPx6XG9GJFoYkRhCZHDnLPPscG0VJCnN1BpxN1J0Wp+28GMKkoQlQVC0VpBECCGEEEJ0Oacc6BRF0QOvAVOAHGCToijLVFXNaHbaNmCUqqo1iqL8L/ACcG17Biw6jrO8nMKnn6Zi5Sp8hwwhdsF8fPr1O+59Fm1d5AlzzR2uOHxaw1xZtZ2tR8rZ0tA2YEeOlTqHNvsWFezDyF4h/GZcb0b0CmFQbDA+hh68/O9UCpIkTfGuInkyBUmEEEIIIUSX054ZujFApqqqWQCKonwEzAY8gU5V1f82O/9n4KZ2PJ/oQJXr/kv+E4/jstqI+OMfCbv1tyiG4/91sdZZya/Ob/VYQXXBKY/F5VbZX1jpWTq5LdvKoZJqAAw6hUGxwVw3OlHb/9YrhFizb4cs6Txt0j8+uSIfrRYkaZh58ypIYtKWQzYWJPHsb0tqX0ESIYQQQgjR5bQn0MUBzRtC5QBjj3P+b4FVrR1QFOU24DaAxMTEdgxJtJerspLC+QuwLV6MT0oKiW+/jW9q6nHv43A7+Hjfx7y+/fU2z/kl/eVstQ62ZWs937YeKWf7UStV9dosU3igieGJIVw7OoERiSEMiTPjZ+rGs2/pH3uX4bcdhWV/gMLdWrg7XkGSoFhtlm3wVU2BLSwJLIlSkEQIIYQQ4izRnkDX2hRIqyUzFUW5CRgFXNDacVVV3wLeAq3KZTvGJNqh+scfyXv0MZyFhYT9/nYi7rgDxXT8ZXg/5v3ICxtf4KDtIGNjxnJuzLm8ueNNr2WXvnpf7h5xd6v3d7tVskqq2HrE6ilgcqCoCgCdAqnRwVw2PNZTfTIx1L97zb6dyJp53j3VAJx18MNftK9NgVoRkoQxMPzGptk2KUgihBBCCCFoX6DLAZqXOYwH8o49SVGUycCjwAWqqta34/nEGeKuqaHoxRcp//eHmPr0ofeH/8Zv6NDj3ie7IpuFmxfyzdFviA+M5y8X/YWJCRNRFIWcYhOfHXobt74cnSuEGQm/49K+lwJQVe9kx1GrZ//btmwrtlqtEbnZz8iIRAuzhmoBLi3BQqBPDyzEWnoQ9q2EfV9q7QFapcC9e6QgiRBCCCGEOK72vFveBCQritIHyAWuA25ofoKiKMOBvwEXq6pa1I7nEmdIzZYt5D38CI6jRwm9+WYi7vkjOt+2K0BW2at4a+dbfJDxAUadkbtH3M2vBv4KH73Ws23Jtlw++m8EtY6HPPf5Z5bC/oM/UVbjZF9BBQ19u0mODOSSwdGMSNT2vvUND0DXExt3u11wdKMW4vZ/CSX7tdsjB4FPMNRXtLyPOR6CYzp2nEIIIYQQots55UCnqqpTUZS7gNVobQveVVV1t6IoTwGbVVVdBiwEAoFPGpbJZauqOus0jFu0k7u+nuJFr1D23nsY4+Lo9X/v4z96dNvnq26WZi5l0dZFlNaVMqvfLO4ecTeR/pFe5y1cvY9ah8vrNodL5ceDZYxPDmfKxGRGJFoYnhCC2d94Rq6tS6ivhIPrYN8q2L8aastAZ4Te42D0rdD/Ygjp1XIPHYDRTyuMIoQQQgghxAm0az2bqqorgZXH3PZ4s68nt+fxxZlRu3MXeXPnYj94EMt11xL1wAPoAgLaPH970XYWbFzA7tLdpEWk8erEVxkSMaTFeVnFVZ7eb6354LfHq5nTA1iPajNw+1bB4e/AZQdfC/SfpgW4pEnga/a+T2M1y5OpcimEEEIIIcQxeuAGJdEW1W6n5M03KfnbWxjCw0l4+20Czx/f5vkF1QW8vOVlVh5aSaRfJPPPn8/0PtPRKd5NxYsq6li09gAfbTqKQuuVcWItfqf3YroCtxvyt2l74fatgsKd2u2h/WDMbZAyHRLGgv4E/8zSrpEAJ4QQQgghTokEurNE3b795M2dS/2ePZhnzybq0UfQBwe3fq6zjn/s/gfv7noXl9vFbWm38dvBv8Xf6O91XmWdg7fWZ/H37w7hcLm5cWwiSZGBzF+512vZpZ9RzwPTUs7o9XUYRy1kfQv7V2lBrqoAFJ0W3KY8pYW48OTOHqUQQgghhDhLSKDr4VSXi9J33qX41VfRBwcT/9pfCZo0qfVzVZWvjnzFS5tfIq86jym9pnDvyHuJD4r3Oq/e6eKfP2fz13UHKK9xMHNoLPdN6U/vcG3ZZrCvkYWr95FnrSXW4scD01K4bHjcGb/WM6aqqGEp5ZfavjhnrdZOIGkS9L8EkqdCQFhnj1IIIYQQQpyFJND1YPWHDpE/92Fqd+wgaNo0ouc9gSEkpNVz95btZcHGBWwp3EL/kP68O/5dRkd7F0lxu1WW7sjlz1/tJ6e8lnFJYcy9eABD4r33hV02PK57BzhVhaIMbRnlvlWQuwVQwZwAw2+ClEug93gw+HT2SIUQQgghxFlOAl0PpLrdlP/zXxS99BKKjw+xf36R4OnTW23IXVZXxqvbXuWz/Z9h9jHzp3P+xJXJV6LX6ZseT1X5dn8xz3+5jz35FQyKDWb+FUM4PzmiIy/rzHLa4cgPDVUpV4E1W7s9dgRc9IgW4qIGS084IYQQQgjRpUig62HsObnkP/IINRs3EnjBBUQ//RTGyMgW5zlcDj7c+yFv7niTWmctNw64kd8P/T1mH+/Zth1HrSxYtZefskpJCPVj0XXDmJkW2zP6xdWUQeYarT9c5lqtH5zBF/peCOPv1SpTSi84IYQQQgjRhUmg6yFUVcX66acUzV8AikLMs89gvuKKVmflvsv5jhc2vcDhisOMix3Hg6MfpK+lr9c5h0qqeXH1PlbszCc0wMS8mQO5YWwvTAZdi8frVkoPNi2lzP4JVBcERMLA2VpBk74Xgsn/RI8ihBBCCCFElyCBrgdwFBaS/6c/Ub3+O/zPOYfYZ5/BGNdyD9sh2yEWblrId7nf0Su4F69Neo3z4873Cn1FlXW8svYAH248io9Bx5xJyfzu/D4E+XbTJuBuFxzdqM3C7f8SSvZrt0cOgvH3aEspY0eArpsHVSGEEEIIcVaSQNeNqapKxRdfUPD0M6h2O1GPPUbIDdejHBNOKuwV/G3H3/j3nn/ja/Dl/lH3c0PqDRj1TSGtss7B2+uzeLtZC4I/TEwmIqgbFv6or9SqUe5bBftXQ20Z6IzQexyMvlVbShnSq7NHKYQQQgghRLtJoOumnKWlFMx7ksqvv8Zv+HBi5z+HqXdvr3NcbhefZ37Oq9tepbyunCuSr+Cu4XcR7hfuOafe6eLfG7J5dV0mZdV2ZqTFcP/UFE8Lgm7DerShtcAqOPwduOzga4H+07QAlzQJfM0nfhwhhBBCCCG6EQl03VDFV19RMO9J3JWVRD5wP6G//jWKXu91zuaCzTy/6Xn2lu1lROQI3pj8BgPDBnqOu90qy3bk8eev93G0rJbz+oUx95JU0uItHX05p8bthvztTVUpC3Zqt4f2gzG3aUspE84BvfwVF0IIIYQQPZe82+1GXDYbBc88S8Xy5fgOHEjs+//AJznZ65y8qjxe2vISqw+vJjogmoUTFjKt9zTPPjlVVVl/oITnV+0lI7+CgTHB/N8tQzg/ObzVAipdiqMWsr7VAty+L6GqABQdJIyFKU9pRU3Ck0/8OEIIIYQQQvQQEui6iar168l/7E84y8oIv+suwm+/DcXYtAeuxlHDe7vf471d76GgcMfQO/j14F/jZ/DznLPjqJXnv9zLjwdLiQ/pJi0IqooallJ+qe2Lc9aCKVBbQtn/EkieCgFhnT1KIYQQQgghOoUEui7OVVVN0fPPY/3kE3ySk4h/43X8Bg3yHFdVlVWHVvHSlpcorCnkkt6XcM/Ie4gJbOqfdqikmhe/2seKdK0FwRMzB3LD2ER8DPrWnrJzqSoU7dGqUu5bBblbABXMCTD8Jm0pZe/xYOiGxVqEEEIIIYQ4zSTQdWHVGzaS/8gjOPLzCfvdrYT/4Q/oTCbP8d2lu3l+4/NsK9rGgNABPD/heUZGjfQcL6qs49W1mXy4MRtTV25B4LTDkR8aZuJWgjVbuz12BFz0iBbiogZDV18SKoQQQgghRAeTQNcFuWtrKXrpZco/+ABjr0R6/fOf+I8Y7jleUlvCK1tfYUnmEkJ8Q3jyvCeZ3W82ep0241ZZ5+Dt7w7x9++ysDvdXD8mkT9MSiIyyLezLqmlmjLIXKMFuMy1UF8BBl+tsff4e7XKlMExJ3oUIYQQQgghzmoS6LqYmm3byJ/7MPYjRwi56SYi770Hnb8/AHaXnX/t+Rd/S/8b9a56bh50M7el3UaQKUg77nTz7w1HeHVdJqXVdi5taEHQp6NbEKR/DGufAlsOmONh0uOQdg2UHtSWUe5bBdk/geqCgEgYOFsraNL3QjD5d+xYhRBCCCGE6MYk0HURbrudklf/Suk772CMjibxH+8RcM45gLZP7tucb1m4aSHZldlcEH8B94+6n97m3tp93SrL0/N48SutBcG5fbUWBEMTOqEFQfrHsHyOVpESwHYUlvwvfPUnrSolQOQgGH+PtpQydgQc0whdCCGEEEIIcXIk0HUBdRkZ5D00l/oDB7BcfRWRDz2EPjAQgIPWg7yw6QV+zPuRPuY+vDH5DcbHjffc97sDxSxYtZfdeRUMiAnm/VuGMKGzWhC4XfDVY01hznO7E+rK4ZIXtKWUIb06fmxCCCGEEEL0QBLoOpHqcFDy1luUvPEmhpAQEv72JoEXXACArd7GGzve4KO9H+Fv8Oeh0Q9xbeq1GHVaQZP0HK0FwQ+ZWguCv1w7jFlDO7gFQVUR5GyGnE3aR+5WcFS3fq7TDmNv77ixCSGEEEIIcRaQQNdJ6jMzyXtoLnW7dxM8YwbRjz2K3mLB6Xby2f7P+Ov2v1Jhr+Cq5Ku4c/idhPqGAnC4oQXBF+n5hPgbeXzGQG48pwNaEDjtULCzKbzlbALrEe2YzgDRQ2D4jbDzU6gta3l/c/yZHZ8QQgghhBBnIQl0HUx1uSj7x/sUL1qELiCAuEWLCJ42FYCN+RtZsGkBB8oPMDp6NA+NfoiU0BQAiivreXXdAf69IRujXseciUn8bkLfM9OCQFW1giY5m5pm4PJ3gKteOx4cB/GjYMzvIH40xAwFY0MD8/jR3nvoQDs26fHTP04hhBBCCCHOchLoOpD9yBHyHn6E2q1bCZw8iZgnn8QQFkZOZQ5/3vxn1mSvIS4wjpcufInJiZNRFIWqeidvr8/i7e+yqHe6uX5MAnMmJhMZfBpbENirIW97s9m3zU0FTAy+EDMMxt6mhbW4UWCOa/ux0q7RPrdW5VIIIYQQQghxWkmg6wCq2035hx9S9OKfUQwGYl94nuCZM6l11vL61ld4f/f76HV6/jD8D/zPwP/B1+CL3enmw41HeGXtAa0FwZAY7pvan74Rge0cjKq1D2i+dLJwt9ZCACCkD/SZoIW3+FFaQ2+D6fiPeay0ayTACSGEEEII0QEk0J1hjrw88h59lJqffiZg/HhinnkafVQkX2R9wctbXqa4tpiZfWdy94i7iQqIwu1WWbYjjxdX7yO7rIZz+obyziUDGHaqLQjqbJC7xbt4SW25dswUBHEjtBYCjQEuIPz0XbwQQgghhBDijJJAd4aoqopt8ecUzp8PbjfRTz2J5eqr2Vmyk+dX3kd6STpDwofw8kUvMzRiKADfHyhhwZd72JVbQWp0EP/4zWgu6B9x8i0I3C4o3uu9dLJ4H6ACCkSkQuqMhvA2GiJSQHeGi6kIIYQQQgghzhgJdGeAo6iIgsefoOqbb/AfPZqY+c9hDTXx2A+PsezgMsL9wnlm3DPM7DcTnaJjZ46N57/cy/eZJcRZ/Hj52qHMHhp34hYEVcWQe0zbAHuVdswvVAttg6/SZt7iRoCv+cxfvBBCCCGEEKLDSKA7zSpWrqTgyadw19UR9cjD+F9/Nf+391+89e1bON1Ofjv4t/wu7XcEGAM4UlrNi1/tZ/mOPEL8jfxpxkBuaqsFgdMOhTu9l06WH9aOKXqtbcDQ65uWTob2hc5oLi66BIfDQU5ODnV1dZ09FCGEEGcZX19f4uPjMRrPQCVuIUQLEuhOE2d5OQVPPkXll1/iOzSNmPnz+UF/iIXLLie3KpeJCRO5f9T9JAQnUFJVzwsrd/GvDdkY9Ap3XZTEbRf0JbixBYGqQkWud9uAvO1NbQMCoyFhNIz6bVPbAJN/51286HJycnIICgqid+/eJ79kVwghhGgnVVUpLS0lJyeHPn36dPZwhDgrSKA7DSrXrSP/T4/jqqgg4t57Kb18PHdtfY4NBRtIsiTx1pS3ODf2XKrqnfxlzX7eXp9FndPNdaMTuHtSMpG+bsjf5L33rTJfe3C9D8QOa+j5NkoLcMFxMvsmjquurk7CnBBCiA6nKAphYWEUFxd39lCEOGtIoGsHV0UFhc/Nx7ZkCT4DBhDyxl94s+ZLPll1HUGmIB4d+yhX9b8Kt1vH//10mFfWHqCkqp6bU1zckVxOlG0tfLgJCnY1axvQG3qPb9Y2YMgvbxsgBEiYE0II0Snk/x8hOpYEulNU9f0P5D/2GM7iYkL+93bWXRTKa7vvptpRzXUp13HHsDsIMgazeut+vl6zitjKXbwVeJg0cyaGI+VwBDAFNrQN+GNT0+7AiM6+NCGEEEIIIUQ3IYHuJDiKisi99z7iX34Jnb8/hQsXYv3oP5j69aPi8Vt5uOITDm47yDnRY3mo39Uk2Qoo/Ph+rIc3MM2ZzSWKCkZQg1NR4i9t1jYgVdoGCCGEEILDhw8zY8YMdu3addof+5tvvuHFF1/kiy++YNmyZWRkZDB37txTeqzevXsTFBSEXq/HYDCwefPm0zxaIcQvpevsAXR56R9T8vsLqN28ifybJpA1fQrW/3yM8aarePWOGG49ugB7VSGvEMlbW74k6Z/XwPI5+GSuIE8NY2/qnbhu/BweOoJy5waY/RqM/DVEDZIwJ7qMJdtyGbdgHX3mrmDcgnUs2ZZ7xp9z+vTpWK1WrFYrr7/+uuf2b775hhkzZpzx5/+l5s2bR1xcHMOGDWPYsGGsXLmycwaS/jG8PBjmWbTP6R+f8afsbq/VJ598wqBBg9DpdC3ebM6fP5+kpCRSUlJYvXp1p4xvRdYKpn46lbT305j66VRWZK0448/Z3V7D4/176wqvIWi/7D18069wdrO9YrNmzTrlMNfov//9L9u3b5cwJ0QX0a5ApyjKxYqi7FMUJVNRlBY/HRRFmaAoylZFUZyKolzVnufqFOkf8+WyB7Te3ChUHoHqqlJ+vt6fq+OX8FPRj9xTVs6Sg/sYV1XH9/4Tudf+e2Ypi/h00ncMf3gNA69/Fn3yRPCzdPbVCNGqJdtyeXjxTnKttahArrWWhxfvPOOhbuXKlVgslhZvMDuS0+n8Reffc889bN++ne3btzN9+vQzNKrjSP8Yls8B21FA1T4vn3PGQ113e60GDx7M4sWLmTBhgtftGRkZfPTRR+zevZsvv/ySO+64A5fLdbqHelwrslYw78d55Ffno6KSX53PvB/nnfFQ191eQ2j931tXeA0blbz+BrVbtlD8+hun7TGdTic333wzaWlpXHXVVdTU1PDUU08xevRoBg8ezG233YaqqgC88sorDBw4kLS0NK677joAqqurueWWWxg9ejTDhw9n6dKlLZ7jH//4B3fddRcAv/71r5kzZw7nnXceffv25dNPP/Wct3DhQkaPHk1aWhpPPPHEabtGIcTpd8pLLhVF0QOvAVOAHGCToijLVFXNaHZaNvBr4P72DLKzrPjuKQ7t8idO+9mJW4H1STre6lXPZboQ5iRcjDHqPBbuDeQfm4vQ6xR+e0Ef5l3Qr6kFgRCd7Mnlu8nIq2jz+LZsK3aX2+u2WoeLBz9N58ON2a3eZ2BsME/MHHTc533hhRfw9fVlzpw53HPPPeyvO9NtAAAgAElEQVTYsYN169axdu1a3nvvPb7//ns2b97M3LlzOXjwIMOGDWPKlClceumlVFVVcdVVV7Fr1y5GjhzJP//5zzY32ffu3Zubb76Z5cuX43A4+OSTT0hNTaWsrIxbbrmFrKws/P39eeutt0hLS2PevHnk5eVx+PBhwsPDmTp1KkuWLMHlcrFr1y7uu+8+7HY7H3zwAT4+PqxcuZLQ0NAT/CmfJqvmQsHOto/nbGpqX9LIUQtL74It77d+n+ghcMmC4z5tT3utBgwY0OrzL126lOuuuw4fHx/69OlDUlISGzdu5Nxzzz3un88v8fzG59lbtrfN4+nF6djddq/b6lx1PP7D43y6/9NW75MamspDYx467vP2tNewLR3xGhY89xz1e9p+DQFUu53a9HRQVawffUT9nj0ox+m55jMglehHHjnhc+/bt4933nmHcePGccstt/D6669z11138fjjjwPwq1/9ii+++IKZM2eyYMECDh06hI+PD1arFYBnn32WiRMn8u6772K1WhkzZgyTJ08+7nPm5+fz/fffs3fvXmbNmsVVV13FV199xYEDB9i4cSOqqjJr1izWr1/PhAkTUBSFqVOnoigKt99+O7fddtsJr0sIcWa1Z4ZuDJCpqmqWqqp24CNgdvMTVFU9rKpqOuBu7QG6uvccLs7fBf/P3p3HR1XdjR//nJnsewKEBMIWErYEBGQREesOSG3RuosooLa2v6f0qY+t1iK07tWnFVtrH4UgoogL4oaK+wKi7GLCFghbQjay7zOTOb8/7s1kspGQhUmG7/v1yiszZ+7c+525lzDfOd9zjo8ZvVXDtDSIL3Zw3/Wfs9pxHVNfrSZlax7XTRjAV/dezL3TR0gyJ3qUxslca+1tdeGFF/LNN98AsG3bNsrLy7Hb7WzcuJFp06a5tnv88ccZOnQou3bt4sknnwRg586dPP300+zZs4eMjAw2bdp0ymP17t2bHTt2cPfdd/PUU08BsHjxYsaNG8fu3bt59NFHmTt3rmv77du3884777B69WoAUlNTWb16NVu2bOGBBx4gKCiInTt3MmXKFF566SXX8/71r38xZswY5s+fT1FRUYfen3ZpnMy11t5G3niumpOVlcWAAQNc9+Pi4sjK6vryYneNk7nW2tvKG89hc//eusM5BLCdONHwfifFMGDAAKZOnQrAnDlz2LhxI1988QWTJ09m9OjRfP7556SlpQEwZswYbrnlFl5++WV8fIzv5z/++GMef/xxxo4dy0UXXUR1dTXHjjX/xVyd2bNnY7FYGDVqFLm5ua79fPzxx4wbN47x48ezb98+0tPTAdi0aRM7duzgww8/5Nlnn+Xrr7/ulNcuhGi/jkyK0h847nY/E5jcnh0ppe4C7gIYOHBgB0LqXBd8D0o3bLNouPg7+EnZF5wstzEzOYb/mT6coX1CPBOkEK1orSdt6uOfk1Vc1aS9f0Qgr/2y/d96n3vuuWzfvp2ysjL8/f0ZP34827Zt45tvvuGZZ57hsccea/G5kyZNIi4uDoCxY8dy5MgRLrjggha3v+aaa1zHfOuttwDYuHEja9euBeCSSy6hoKCAkpISwBhDEhgY6Hr+xRdfTGhoKKGhoYSHh3PVVVcBMHr0aHbv3g3A3XffzaJFi1BKsWjRIu655x5SUlLa+/Y0r5WeNP6RbJZbNhI+AOa1v2TP285VS+pK1dx19vTqrfWkXfHmFWRXZDdpjw2OZcWMFe0+rredw5b+vZ2Jc9haT5o9L49Dl18BdbFojbO0lP5//198+nRspurGr0Upxa9//Wu2bdvGgAEDWLJkCdXV1QCsX7+er7/+mnfffZeHHnqItLQ0tNasXbuW4cOHN9hPXaLWHH9/f9ftuvdXa83999/PL3/5yybb9+vXD4Do6GiuvvpqtmzZ0qS8WQhxZnWkh665v6BN/9K2gdb6ea31BK31hD4d/GPYmUadsOLbqDTftxaGZ1qI7xPCW78+n+fmnCvJnOjR7p0+nEDfhhP0BPpauXf68Bae0Ta+vr4MHjyYFStWcP755zNt2jS++OILDh061GJJXB33DxhWq7XVsTd127tve6oPfsHBwS0ez2KxuO5bLBbX/vr27YvVasVisXDnnXeyZcuWU8bUJS59EHwDG7b5BhrtHeBt56olcXFxHD9enxBnZma6PpyeKQvHLyTAGtCgLcAawMLxCzu0X287hy39e+sO5/Dkv59DOxtWMGins1PG0h07dozNmzcD8Oqrr7oS6969e1NeXu4a4+Z0Ojl+/DgXX3wxf/vb3yguLqa8vJzp06fzz3/+03U+du7c2a44pk+fTkpKCuXl5YDRM5qXl0dFRQVlZWWAMV7v448/Jjk5uUOvWQjRcR1J6DKBAW7344ATLWzbIz1w9Vxu/KM/19/v4/q58Y/+3D/7Vl676zzGD4z0dIhCdNjscf157JrR9I8IRGH0zD12zWhmj+vf4X1feOGFPPXUU1x44YVMmzaN//znP4wdO7bBt9ChoaGuDwid6cILL+SVV14BjJn8evfuTVhYWLv3l51d36uybt06z3yIGXM9XPWM0SOHMn5f9YzR3kHedK5a8rOf/Yw1a9ZQU1PD4cOHSU9PZ9KkSZ1+nFOZFT+LJecvITY4FoUiNjiWJecvYVb8rA7v25vOYUv/3rrDOazatQvs9oaNdjtV7Uye3I0cOZKVK1cyZswYCgsLufvuu7nzzjsZPXo0s2fPZuLEiQDU1tYyZ84cRo8ezbhx4/jv//5vIiIiWLRoEXa7nTFjxpCcnMyiRYvaFccVV1zBzTffzJQpUxg9ejTXXnstZWVl5ObmcsEFF3DOOecwadIkZs2axYwZMzr8uoUQHdORksutQKJSagiQBdwI3NwpUXUTJ3OSsFZei3+fDSjfYrQ9gqr86dSWju70Eg8hPGn2uP6dksA1Nm3aNB555BGmTJlCcHAwAQEBDcbzAPTq1YupU6eSnJzMzJkzmTWr4x9swZj2fN68eYwZM4agoCBWrmxh0pA2+sMf/sCuXbtQSjF48GD+7//+r1PiPG1jru+UBK4xbzpX69at47/+67/Iz89n1qxZjB07lg0bNpCUlMT111/PqFGj8PHx4dlnn8VqPfPLx8yKn9UpCVxj3nQOW/r31h3OYfzb67pkv4MHD2bPnj1N2h9++GEefvjhJu0bN25s0hYYGNjs36aLLrqIiy66CDBmtrz99tsBY8ZLd3U9cgALFy5k4cKmPcc//PDDqV6GEMIDVHNlEm1+slJXAk8DViBFa/2IUuqvwDat9btKqYnAOiASqAZytNanHNAzYcIE3V3WNTnV2KJN913igYiEaJu9e/e2WmYlhBBCdBX5f0iIjlFKbddaT2jLth3poUNr/QHwQaO2B91ub8UoxeyR7p0+nPvf+pEqe/1Aus4YWySEEEIIIYQQnaFDCZ23qytBe3LDfk4UV9EvIpB7pw/vktI0IcSpXX311Rw+fLhB2xNPPMH06dM9FJFoiZyrnk/OoRBC9BwdKrnsCt2p5FKInmrv3r2MGDFCxnoKIYQ447TW7Nu3T0ouheiA0ym57Mgsl0KIbiogIICCgoJmpxIXQgghuorWmoKCAgICAlrfWAjRKaTkUggvFBcXR2ZmJvn5+Z4ORQghxFkmICDAtVi9EKLrSUInhBfy9fVlyJAhng5DCCGEEEJ0MSm5FEIIIYQQQogeShI6IYQQQgghhOihJKETQgghhBBCiB6q2y1boJTKB456Oo5m9AZOejoI4dXkGhNdSa4v0ZXk+hJdSa4v0ZW66/U1SGvdpy0bdruErrtSSm1r61oQQrSHXGOiK8n1JbqSXF+iK8n1JbqSN1xfUnIphBBCCCGEED2UJHRCCCGEEEII0UNJQtd2z3s6AOH15BoTXUmuL9GV5PoSXUmuL9GVevz1JWPohBBCCCGEEKKHkh46IYQQQgghhOihJKETQgghhBBCiB5KEro2UErNUErtV0odVErd5+l4hPdQSg1QSn2hlNqrlEpTSi30dEzC+yilrEqpnUqp9z0di/A+SqkIpdSbSql95t+yKZ6OSXgPpdR/m/8/piqlXlVKBXg6JtFzKaVSlFJ5SqlUt7YopdQnSql083ekJ2NsD0noWqGUsgLPAjOBUcBNSqlRno1KeBEHcI/WeiRwHvAbub5EF1gI7PV0EMJrLQU+0lqPAM5BrjXRSZRS/YHfAhO01smAFbjRs1GJHu5FYEajtvuAz7TWicBn5v0eRRK61k0CDmqtM7TWNmAN8HMPxyS8hNY6W2u9w7xdhvFBqL9noxLeRCkVB8wClnk6FuF9lFJhwIXAcgCttU1rXezZqISX8QEClVI+QBBwwsPxiB5Ma/01UNio+efASvP2SmD2GQ2qE0hC17r+wHG3+5nIB27RBZRSg4FxwPeejUR4maeBPwBOTwcivFI8kA+sMMt6lymlgj0dlPAOWuss4CngGJANlGitP/ZsVMIL9dVaZ4PxRTsQ7eF4TpskdK1TzbTJWg+iUymlQoC1wO+01qWejkd4B6XUT4E8rfV2T8civJYPMB54Tms9DqigB5Yrie7JHMv0c2AI0A8IVkrN8WxUQnQ/ktC1LhMY4HY/DunuF51IKeWLkcy9orV+y9PxCK8yFfiZUuoIRrn4JUqplz0bkvAymUCm1rqusuBNjARPiM5wGXBYa52vtbYDbwHnezgm4X1ylVKxAObvPA/Hc9okoWvdViBRKTVEKeWHMRj3XQ/HJLyEUkphjD3Zq7X+u6fjEd5Fa32/1jpOaz0Y42/X51pr+XZbdBqtdQ5wXCk13Gy6FNjjwZCEdzkGnKeUCjL/v7wUmXRHdL53gdvM27cB73gwlnbx8XQA3Z3W2qGU+n/ABozZlVK01mkeDkt4j6nArcCPSqldZtuftNYfeDAmIYQ4Hf8FvGJ+6ZkBzPNwPMJLaK2/V0q9CezAmBV6J/C8Z6MSPZlS6lXgIqC3UioTWAw8DryulFqA8SXCdZ6LsH2U1jIcTAghhBBCCCF6Iim5FEIIIYQQQogeShI6IYQQQgghhOihJKETQgghhBBCiB5KEjohhBBCCCGE6KEkoRNCCCGEEEKIHkoSOiGEEF5LKVWrlNrl9nNfJ+57sFIqtbP2J4QQQrSHrEMnhBDCm1Vprcd6OgghhBCiq0gPnRBCiLOOUuqIUuoJpdQW8yfBbB+klPpMKbXb/D3QbO+rlFqnlPrB/Dnf3JVVKfWCUipNKfWxUirQYy9KCCHEWUkSOiGEEN4ssFHJ5Q1uj5VqrScB/wKeNtv+BbyktR4DvAI8Y7Y/A3yltT4HGA+kme2JwLNa6ySgGPhFF78eIYQQogGltfZ0DEIIIUSXUEqVa61Dmmk/Alyitc5QSvkCOVrrXkqpk0Cs1tputmdrrXsrpfKBOK11jds+BgOfaK0Tzft/BHy11g93/SsTQgghDNJDJ4QQ4mylW7jd0jbNqXG7XYuMTRdCCHGGSUInhBDibHWD2+/N5u1vgRvN27cAG83bnwF3AyilrEqpsDMVpBBCCHEq8k2iEEIIbxaolNrldv8jrXXd0gX+SqnvMb7cvMls+y2QopS6F8gH5pntC4HnlVILMHri7gayuzx6IYQQohUyhk4IIcRZxxxDN0FrfdLTsQghhBAdISWXQgghhBBCCNFDSQ+dEEIIIYQQQvRQ0kMnhBBCCCGEED2UJHRCCCGEEEII0UNJQieEEF1EKTVYKaWVUj7m/Q+VUre1Zdt2HOtPSqllHYn3bKeU8ldK7VFKxXg6lp5MKfWlUuoO8/YtSqmPu+AYnXK9K6XGKKW+7YyYhBDCUyShE0KIFiilNiil/tpM+8+VUjmnm3xprWdqrVd2QlwXKaUyG+37Ua31HR3d91nuLuBrrXUOgFLqRaXUwx6OqQG3xH99o/aXlVJLPBRWi7TWr2itr+jIPrryetda7waKlVJXdXRfQgjhKZLQCSFEy14EblVKqUbttwKvaK0dZz6ks0t7eyzb6ZfAqjN4vFNq5bWfp5Sa2sXHOFu8gnHuhRCiR5KETgghWvY2EAVMq2tQSkUCPwVeMu/PUkrtVEqVKqWOn6qXpFEpmlUp9ZRS6qRSKgOY1WjbeUqpvUqpMqVUhlLql2Z7MPAh0E8pVW7+9FNKLVFKvez2/J8ppdKUUsXmcUe6PXZEKfU/SqndSqkSpdRrSqmAFmIeqpT6XClVYMb6ilIqwu3xAUqpt5RS+eY2/3J77E6317BHKTXebNdKqQS37Vw9YXW9MUqpPyqlcoAVSqlIpdT75jGKzNtxbs+PUkqtUEqdMB9/22xPde95UUr5mq9hbDOvcyAwFPi+pfPXaPul5vkuVUptV0pNM9tjlFKVSqlebtuea8bua96fb74vRWYv8CC3bbVS6jdKqXQg/RQh/A1osffQfO8PKqUKlVLvKqX6neoYZtuvlVLp5vl6yDz3m83X+LpSys/c9pTno1EctyulNpq3/+B2zZYrpexKqRfNxzx5vX8JXKqU8j/F+y2EEN2WJHRCCNECrXUV8Dow1635emCf1voH836F+XgERlJ2t1Jqdht2fydGYjgOmABc2+jxPPPxMGAe8A+l1HitdQUwEzihtQ4xf064P1EpNQx4Ffgd0Af4AHiv7gO52+uYAQwBxgC3txCnAh4D+gEjgQHAEvM4VuB94CgwGOgPrDEfu87cbq75Gn4GFLThfQGIwUikB2GUQVqAFeb9gUAV8C+37VcBQUASEA38w2x/CZjjtt2VQLbWelczxxwNZJxGr+tWYKwZ52rgDaVUgFmu+SXG+1tnDrBGa203r40/AddgnJtvMM6Vu9nAZGDUKY7/LDBMKXVZ4weUUpdgnLPrgViM87OmDceYAZwLnAf8AXgeuAXjnCcDN5nbtXY+mqW1/lvdNYtxLeVj/PsCD17vWusswA4Mb+01CCFEdyQJnRBCnNpK4DqlVKB5f67ZBoDW+kut9Y9aa6c5HudV4Cdt2O/1wNNa6+Na60KMD+AuWuv1WutD2vAV8DFuPYWtuAFYr7X+RGttB54CAoHz3bZ5Rmt9wjz2exjJSRNa64Pmfmq01vnA391e3ySMRO9erXWF1rpaa73RfOwO4G9a663maziotT7axvidwGLzmFVa6wKt9VqtdaXWugx4pC4GpVQsxgf+X2mti7TWdvP9AngZuFIpFWbev5WWSyojgLI2xofW+mUzLofW+n8Bf+oTgpWYiaSZ9N7kdtxfAo9prfeayeOjwFj3Xjrz8ULzC4WWVGO8D8310t0CpGitd2ita4D7gSlKqcGtHOMJrXWp1joNSAU+1lpnaK1LMHrJxpmvvcXz0Rbmv6W3gaVa6w/MfXr6ei/DuAaEEKLHkYROCCFOwUxQ8oGfK6XigYkYPTIAKKUmK6W+MMvPSoBfAb3bsOt+wHG3+w2SHaXUTKXUd2bJXDFG71Jb9lu3b9f+tNZO81j93bbJcbtdCYQ0tyOlVLRSao1SKkspVYqRJNXFMQA42kKv1gDgUBvjbSxfa13tFkOQUur/lFJHzRi+BiLMZGkAUKi1Lmq8E7MnZxPwC2WUic7EGC/VnCIgtK0BKqXuMUsES8zzE079+/IOMMq8Xi4HSrTWW8zHBgFLzdLAYqAQoxfU/dy4Xxen8gLQVzWd0KPx+S/H6B1t7Ri5brermrkfAq2ej7ZYDuzXWj9R19ANrvdQoLiNxxNCiG5FEjohhGjdSxg9c7di9Fq4f9BdDbwLDNBahwP/wfiA3ppsjGSkzsC6G+ZYnrUYPQ19tdYRGGVkdfvVrez7BEbiULc/ZR4rqw1xNfaYebwxWuswjJ6nujiOAwNV8xNrHMcYk9acSowSyTqNlwlo/Pruwej9mmzGcKHZrszjRCm3cX2N1PWWXQdsNsvrmrMbiG/htTSgjPFyf8ToZY00z0+JGQ9mMvo6Rk9Z417B48AvtdYRbj+BWmv3qfNbO7+Yx7EDfwEeouE11/j8BwO9aHj+23SMFpzqfJySUuo+87kL3No8er2b4wv9gP1t2V4IIbobSeiEEKJ1LwGXYYx7a7zsQChGD1G1UmoScHMb9/k68FulVJwyJlq5z+0xP4wSvnzAoZSaCbhP/Z4L9FJKhZ9i37OUUpcqYyKOe4AaoD3rbYUC5RhTu/cH7nV7bAtGYvq4UipYKRWg6mdeXAb8jzImBFFKqQS3ssJdwM3KmBhmBq2X64Vi9BAVK6WigMV1D2itszHKAf+tjMk6fJVSF7o9921gPLAQcyKb5mitMzEmCJnU6CGr+brqfvzMeBwY58dHKfUgxtgvdy9hjNP6GUavZp3/APcrpZIAlFLh5njD9lqFca3McGtbDcxTSo01k6VHge+11kc6cBx3LZ6PUzGv498CsxuVenr6er8I+NwsTxVCiB5HEjohhGiF+UH4WyAYozfO3a+BvyqlyoAHqZ/koTUvABuAH4AdwFtuxyvD+OD7OkYp4M3ux9Va78MYq5dhlu71c9svWuv9GL1S/wROAlcBV2mtbW2Mzd1fMBKiEmB9ozhrzX0nAMeATIzxTGit38AYW7UaY3xS3YyhYCRXV2GUuN1iPnYqT2OMiToJfAd81OjxWzEmtdiHMbnG79xirMLo/RniHnsL/s/cl7v7MJKXup/PMc7bh8ABjFK/ahqVMGqtN2GMBdzhnkhprdcBTwBrzHLFVIxS0HYxz8Fi6t9btNafAYswXnc2Rk/pje09RjNaOx8tuQFj0pK9qn7Gyv90g+v9FoxEWwgheiSldUeqLoQQQojuzexBG6a1ntPKdv7ATuBSs+evo8f9HFittV7W0X2JrqGUGg08r7We4ulYhBCivSShE0II4bXMksCdwK1a66/P4HEnAp9gjK1s8+yZQgghxOmSkkshhBBeSSl1J0Yp5IdnOJlbCXwK/E6SOSGEEF1NeuiEEEIIIYQQooeSHjohhBBCCCGE6KFaXW/nTOvdu7cePHiwp8MQQgghhBBCCI/Yvn37Sa11n7Zs2+0SusGDB7Nt2zZPhyGEEEIIIYQQHqGUOtrWbaXkUgghhBBCCCF6KEnohBBCCCGEEKKHkoROCCGEEEIIIXooSeiEEEIIIYQQooeShE4IIYQQQggheihJ6IQQQgghhBCih5KETgghhBBCdEv2vDyOzLkVR36+p0MRotuShE4IIYQQQnRLJ//9HFXbt5P/7+c8HYoQ3Va3W1hcCCGEEEKIqrQ0it94A7Sm+LXXsOfk4NunD9bwMCxhYVjDw7GGhTe6H4YlJARlkT4LcfaQhE4IIYQQQnQbNRkZFCxbTsm6daC10eh0UrVtG9W+vtSWloLD0fIOLBYsoaFYw8KMn/BwLOFhRvIXFlafADZIBiOM28HBkgyKHkcSOiGEEEII4XFVP/xAwbJllH36Gfj5gcUCtbWux7XNxtAP1mPt3RtdWUltaanxU1KCs7SU2hLzfmkJzhKjve6+/cQJ1/atJYPW0FAsZm+fNSysYTIYEd4gGbSGhWGpSwxDQlBKnYF3SoiGJKETQgghhBAeobWmYuNGCl5YRuWWLVjCw+l996+wZedQ+v77DRM6p5P8fz9H7OIHUcHBWIKD8Y2NPe3jNU4GGyaETZNDVzJYUtIgnibqksGI8FP3BtYlg3UlouHhRs9gJySD9rw8sn5/D3H/+Ds+ffp0eH/ezlveL0nohBBCCCHEGaUdDko3bKDghWXU7NuHT9++RP/xj0Rcdx3WkGAyZl8NdnvDJ9ntVO3c2aHjKqU6lAw6Kypxlpo9f00SQPN2cYkrYbRnZtb3DJ4qGbRazZ7BZkpDwyOaTQ6bSwbdJ5GJXfxgR96qs4K3vF9K19UmdxMTJkzQ27Zt83QYQgghhBCikzmrqylZt46ClBXYjx/Hb8gQet1xB+FX/RTl5+fp8LpMi8lgSUnTUlEzAXSWlJxeMhgcjP3ECWPcocVC4PjxWLz4Pe0op81G1fbtoDXKz4+Ezz7tVr10SqntWusJbdlWeuiEEEIIIUSXqi0tpejVNRS+9BK1BQUEjBlD9B/uJfTSS8+KSUiUUlhDgrGGBOPbr99pPddIBivqE7wWSkMrNn1b/ySnE9vhw/gNGNDJr8R72I4fd026417O2xNJQieEEEKIdlmfsZ6lO5aSU5FDTHAMC8cvZFb8LE+HJboRe24ehS+tpHjNazgrKgieNo1ed9xB0KSJMoFIGxnJYAjWkBB8+/dvdht7Xh4lb7nNCgo4y8uJ++cz3arXqbuw5+Vx6PIr6hscDkreeos+v767R75f3v+ViBBCCCE63fqM9Sz5dgnZFdloNNkV2Sz5dgnrM9Z7OjTRDdQcPkz2okUcuuwyCle8SMhPfsKQdW8x8IXnCZ48SZK5Tnby38+hnc4GbXW9TqIpb3u/JKETQggvZ8/L48icW3Hk53s6FOFFlu5YSnVtdYO26tpqlu5Y6qGIRHdQ9WMqmQt/R8aVsyh5513Cr/0FQz/6kP5//18CRo70dHheq2rXri6ZRMZbedv7JSWXQgjh5bxlFi/RveRU5JxWu/BeWmsqN2/m5AsvULn5OyyhofS66y6ibp2DT+/eng7vrBD/9jopgT4N8W+v4+2dWTy5YT8niqvoFxHIvdOHM3tc8yWt3Z0kdEII4cWqUlMpfuMN0JriN98k9IrLCTr3XJn5TLSb1prX97/e8uNo7v3qXuYnz2dkL+mR8Wa6tpayTz6h4PkXqN6zB58+fYi+93+IuOEGrCEhng7vrFJXAl3Xa15XAg1IUteMt3dmcf9bP1JlN2YPzSqu4v63fgTokUmdLFsghBBeqHrfPgpSUih97/0Gg+QBsFrxGzwY/8RE/Icl4p+YSMCwYfjGxaGsVs8ELHqEnIocHtz0IJuzN5MYkcixsmPU1Na4Hve3+jM5djLbc7dTYa/g/H7nsyB5ARNjZAIMb+KsqaHk7XcoSFmO/egx/AYNIuqOBYT//OfyZZGHXPHmFWRXZDdpjw6K5tNrPxIftKQAACAASURBVD2r/v1pralxOCmrdlBR46C8xtHwdo2DJz/aR2m1o8lz+0cEsum+SzwQdVOybIEQQpyFtNZUbPqWwpQUKr79FgIDwWJpuH6Rry+RN9+EPTOL6j17KNuwwZXwqYAA/BMSzERvmPE7MRGf6D5n1YcB0ZTWmvcy3uPx7x/HoR0sOm8R1w27jg8Of9BsiVeprZTX97/Oy3teZsHHCxjdezTzk+dzycBLsCgZvt9T1ZaXU7xmDQUrV1Kbf5KApCSily4l9LJL5cugM0xrTUZJBltytrA1Z2uzyRxAXmUe560+jyHhQ4gPjyc+It51e0DoAHws3ScVsNc6Ka82kq4GP2ZbhZmYuW6bjzVI2mxGm8PZvg6rE8VVnfyqzgzpoRNCiB5O22yUfvghBSkrqNm/H58+fYiceyu2w0coee+9hgO/fX2JuPZa11g6Z2UlNYcOUXPgADUH0qlJP0B1ejq1+SddT7GGh9cnecPM3wkJWMPCzvRLFR5wsuokD21+iM+Pf8746PE8PPVhBoS1bW2rmtoa3jn4DitSV5BZnsngsMHMT57PT+N/iq/Vt4sjF53FkZ9P4UurKFqzBmdZGcHnT6HXnXcSdN558mXPGaK15kjpEbbmbHX9FFQXABAbHEtxTTFVjqbJSLhfOLPiZ5FRkkFGSQZ5lXmux3wsPgwKHdQgyYsPj2dw+GACfQLbFFetU1NhM5OqaiPJcr9d3qhnrMl2bglbjcPZ+gGBYD8rwf4+hAT4EOrvY9w274c0d7uZtp8/u4nskuom++6pPXSS0AkhRA9VW1ZG8euvU/jSKhy5ufgnJhA1bz5hP52Fxc+PjNlXU7NvX5Pn+Y8YQfzb6065b0dRkZngpRvJXrpx21le7trGJzYW/8QEAup684YNwy8+Hou/f6e/VuEZnxz9hIc2P0SFvYLfjv8tc0bOwWo5/Z4Yh9PBp0c/ZXnqcvYV7iM6KJq5o+Zy7bBrCfYN7oLIRWewHTtGQUoKJW+tQ9vthE6fTq877iAwOcnToXk9rTWZZZlsydnClpwtbMvZRl6VkYxFB0YzMXYik2ImMTFmInEhcfz1i5d54+g/UJb6L/C005frBv03iy+51dVWbisnoySD/QUHOVB4iIySwxwrO0xu1Qk0dQmVItTah1Brf4JULL7OWCz2vjht0dTU+DdI1CpstbSFv4+lQVIV7G8kYyEBbrfrkjP3RK3R7WA/H6yWjn+J0HgMHUCgr5XHrhndbcbQSUInhBBezJ6dTeFLqyh+/XWcFRUEnXcevRbMJ/iCC7r023KtNY7sbKrrEjwz4bMdOoSu6wW0WPAbNKi+ZNMco+c3cKCUZPUgJTUlPPr9o3xw+ANG9RrFI1MfISEyocP71Vqz+cRmlqcuZ0vOFkL9Qrlx+I3cMvIWegX26oTIRWeo3rOHgmXLKP1oA8pqJfzqq+k1fx5+gwd7OjSvdqL8hKuE0r2MsldALybGTGRijJHEDQob1ORv/dTHPyfX+S3+fTagfIvR9ghq8qdjrTyXpH5hrp6wup6xJhWJyoHF7yQWvzws/nlY/PKxmr+x1I8189FhBFv6EW7tT5TfAPoGDiQueDDRQdGEBvg22zMW7O+Dn0/3K7Xu7rNcSkInhBBeqHrvXgpSVlD64YegNWEzZxI173YCkzz7bbm227EdO9agJ6/6wAHsx47Xj8/z98d/6FC3RM8o3/SJjpaSrW7mm8xvWPLtEgqrC7nrnLu4Y/Qd+Fo6vzzyx/wfSUlN4bNjn+Fn9ePqhKu5Lek24kLjOv1YonVaayq/30LBCy9QsWkTluBgIm+6kci5c/GNjvZ0eF4ptyLXlcBtydlCVnkWAJH+kUyImeBK4OLD41v8O1lSaeezfbn8/vUfWjzOtMTeBPu1UpLYzGP+Phac2smJihMcLjlMRnGGq3QzoySDMluZ6xjBvsHEhzcs3YyPiKd/SP9uNU6vJ5GETgghvITWmoqNmyhckULFt5uxBAURcd11RM29Fd/+3eebxOY4q6qoOXioYdnmgQMNFji3hIfjn5jgmmmzbiIWa3i4ByM/O1XYK3hq21O8eeBNEiISePiCh0nq1fqXBfa8PLJ+fw9x//g7Pn36nPZxM0oyeDH1Rd7LeA+tNdMHT2d+8nyGRw1vz8sQp0k7nZR9+ikFy5ZTvXs31t69iZo7l8gbb5Bxsp3sZNVJV/K2NWcrR0uPAhDqF8rEvhOZFGuUUCZEJJxy8qC8smo+2ZPLR6k5bD5UgMOpsSia9rrRdWPCtNYUVBc0SfIyijPIr6r/G+9r8WVQ2CBXgleX7A0KG0SAT0Cnx+VNJKETQogeTttslHzwAYUpK6g5cMA10UnkDT3/Q5ajqMjVk+cap5eejrOs/tten759G8y06T8sEf+hQ7EEyAeArrA1ZyuLNi3iRPkJbk++nd+M/Q3+1raNhcxe8heKX3uNiBtv7NDC9bkVuazas4o3DrxBpaOSaf2nsWD0AsZHj5de3C7gtNkofe89CpYtx3b4ML4DBtBrwQLCr54t42A7SVF1UYMELqMkA4AQ3xDO7XuuqwduWOSwVsemHi+sZENaDhvScth2tAitYXCvIGYkxzIzOYaM/HL+tC61W4wJK7OVuZK7wyWHXcleVnkWTm2M01Mo+of0b5DkDQkfQnxEPGF+Pfv/uM4iCZ0QQvRQtaWl9ROd5OXhn5hI1Pz5hM+6EuXF6ztprXHk5DTozas+YI7Ps9mMjSwW/AYObLiswrBh+A0cgPKRkp72qHZU88zOZ3h5z8vEhcbxyAWPMC56XINtnDYbzvJy46eiAmd5ObXl5TgrKrFnZ5O/9Glw1KL8/Ij/6EP8+vXrUEwlNSWs2beG1ftWU1hdyDl9zmFB8gJ+MuAnsuRBJ6gtrzD+xrz4ovE3ZtRIet9xB6FXXCH/jjqopKaEbbnbXElcelE6AIE+gYzvO55JMZOYFDOJEVEj2lSGeDCvjI9Sc/goLYfUrFIARsaGMSMphhnJMQzrG9Lgy47uPiaspraGIyVHGiR5GSUZHC05is1pc23XO7B3w/JNM+nrE3h2LaEjCZ0QQvQw9hMnjIlO3njDmOhkynn0mt/1E510d9rhMMfnNSzbtB07Vj8+z88Pv4ShBDRePy8m5qx977TDUZ98VVTgLK/AWdEwIcvOy2Bj+ifYy0oY7jeQYf5xUFndMHGrqGi47EVrlMJ/5AgCk5IISEomICkJ/+HD2rXYdJWjircPvs3KtJVklWcxNHwo85LncWX8lV0yps/bOQoKKFy1iqLVr+IsLSVo8mR63XknwVPPP2v/nXRUma2MHbk7XD1w+wr3odEEWAMYGz3WNQtlUu+kNl2zWmvSTpTyYWo2H6XmcCi/AoDxAyOYkRzD9KQYBvXyvllha521ZJVnNSjbrEv6yu31MyuH+oYyJHyIqyevrmevf0j/ds2+291JQieEED1E9Z49FKx4kdIPPgAg7Mor6TXvdgJGjfJwZN2bs7raXD+v4dIKjtxc1zaW0NAGM23WjdGzRkScct8dHRPWXtrpxFlZ1SDxapCQlZe7HqstLzeTtIoGvWe15n1d3XR9pebU+Cp8Q8MICIvEEhyMJSTE+AkOwhoSgiU4xK09GEtwsNEeEoKzpobj8xfU96ACWK0EjhuHLT2d2pISo83Xl4DERAKSklw/p5PkOZwONhzZwPLU5aQXpRMTHMNto27jmsRrCPINOt23+axjy8ykMGUFxWvXom02Qi+7jF533kHgmDGeDq3HqbRXsiPPTOCyt7KncA9O7cTP4sc50ee4SihH9x6Nn7Vt13etU7PjWJHRE5eaQ1ZxFVaLYvKQKGYmx3D5qBhiws/OUnOtNflV+WSUZHCo+FB9z15xhmsNPgA/ix+DwgfVT8Zi9u4NDh98ytLx9RnrWbpjKTkVOcQEx7Bw/EJmxc86Ey+tTSShE0KIbsyY6GQjBSkpVG7+zpjo5PrrjYlOOliudrarLS6m5uDBJksrOEtLXdv4REc3Kdv0HxqPJdBYSPd0xoRprdE1NfUJlSvRalye6J6UGY/XNk7IKitdvY6novz8miZfwXW365Mva91tV1IWzHFnAU+m/ZMfqw5x+Yir+MOU+9s9XiV7yV8oXru22YXrYx5chD0ri+rUNKrT0qhOS6UqbQ/O5pK8ZLMnb1jiKZM8rTXfZH1DSmoK23O3E+4fzs0jbuamETcRGRDZrtfgzar376fghWXGrLgWC+E/u4peCxbgHx/v6dB6jCpHFbvydrlKKNNOpuHQDnwsPozpPcaVwJ0TfU6bx5wC2BxOvsso4KO0HD5Oy+VkeQ1+VgvTEnszPTmGy0b2JSrYe0vsO0NJTUmDBK+ud+9E+Qk0xt9Ri7LQP6Q/Q8OHMiRiSINk7+vMr1ny7RKqa+u//AqwBrDk/CXdJqnr9IROKTUDWApYgWVa68cbPf4r4DdALVAO3KW13mM+dj+wwHzst1rrDac6liR0QghvpW02StZ/QGFKCjXp6fhERxN121wirruux0900p1prXHk5tYneHXJ3qFD6JoaYyOl8B04AL9Bg6jY9C3U1oLVSsQvfoF21jZNyOp6zioqwOE4dQAAVqsrqbI26PVquTfMGtJ4O6OHrD3li7XOWlakreDZXc8S5hfG4imLuWRgx2a+O92F67XWbkleKtVpac0neWaCF5CURMCwxGbHju7K28Xy1OV8efxLAn0CuSbxGm4bdRuxIbEdek09ndaaqm3bOLlsGRVffW18WXTDDUTdfhu+fft6Orxur6a2ht35u43FvLO3sPvkbhxOB1ZlJal3kquEcmyfsafdO1xlq+Xr9Hw2pObw6d5cSqsdBPlZuXhENDOSYrh4RDQh/jKGsaOqHFUcLT3aIMk7XHKYI6VHcDjr/1ZblMU1QYu72OBYPr724zMZcos6NaFTSlmBA8DlQCawFbipLmEztwnTWpeat38G/FprPUMpNQp4FZgE9AM+BYZprVtcVl4SOiGEt6ktLaXotdcoWvWyMQnBsGFEzZ9H+JXePdFJd6dra+vH55llmxWbNzeYbRMfH3yiok6rFNESZLRbzXZLSAgqIMBj45SOlBzhgU0PsDt/N5cPupw/n/dnogKiPBJLY1pr7JmZZi+e8VOVmubqUVW+vvgPG1af4CUnEZBYn+QdKj5ESmoKH2QYJctXxl/JvKR5nbIIek+inU7Kv/ySgudfoGrXLqxRUUTNvZXIm26SJUBOwV5rZ/fJ3a4xcD/k/YDNacOiLIyMGulK4Mb3HU+w7+mPXSuttvPFvjw+Ss3hy/35VNlrCQ/05fJRfZmRFMMFib0J8PW+sV/dkcPpILMs05XkLd2xtNntFIrdt+0+w9E1r7MTuinAEq31dPP+/QBa68da2P4mYK7WembjbZVSG8x9bW7peJLQCSG8hT0rq36ik8pKgs+fQtS8+QRfMFUmIeiG7Hl5HLr8ivpeO4wF0RM+/eSMjqXrLE7t5NV9r/L09qfxtfrywOQHuHLIld3+2muQ5KWmUpWWRnXanqZJXnIyAUmjCEhKoqhfKKvSX2Vt+lqqHFVcFHcRC0YvYGz0WA+/mq5V1+tfsHwZtoOH8O3fn6j584i45hpXCbGoZ3faSTuZ5iqh3JW3i+raahSK4VHDXSWU5/Y9l1C/0HYdo6C8hk/3GmvEbTpYgK3WSXSoP9PNmSknDYnC1yqztXraFW9eQXZFdpP2ntpD15a+3f7Acbf7mcDkZg76G+D3gB9QV8fRH/iu0XObzJ+qlLoLuAtg4MCBbYlbCCG6raq0NApTVlD60UegFGFXzqTXvHkEjBzp6dDEKZz893NoZ8MSHO10kv/v5zq0vponnCg/wYObHuT7nO+5oP8F/OX8vxAdFO3psNpEKYXfgAH4DRhA2IwZgJnkHT9e34uXlkbphx9S/NprxnN8fbl++HBuHHEpOyNLeaNgG7cf/YJzYs9lwegFTOs/rdsnsqfDWVFB8ZtvUvDiShzZ2fgPG0a/J58kbOYMWXrATa2zlr2Fe40Sypwt7MzdSaWjEoCEiASuSbyGSTGTmBAzgXD/9vdkniiu4uM0Y3mBLYcLcWoYEBXIbecPYkZyDOMGRGKxeM/15w0Wjl/Y7Bi6heMXejCq9mvLv/rmrsAm3Xpa62eBZ5VSNwN/Bm47jec+DzwPRg9dG2ISQohuRWtNxTffUJCygsrvvsMSHEzU3LnGRCexZ/e4np6iateuplP02+1U7dzpmYDaQWvN2wff5omtT6C1ZvGUxfwi8Rc9PplRSuE3cCB+AwcSNnMm0DDJq0pNpTptD9UbPmN4WRl/Bpw+Vo733cmu6F+xMb4vEy66kYunzcU3oOf2XDmKiih6+RWKXn6Z2pISgiZMIHbJYoIvvLDHn+PO4NRO9hfud5VQbs/d7pr2Pj48nquGXuVK4Dpadnz4ZIVrjbgfjhcDMKxvCP/v4gSmJ8cwKjZMzkk3VjfxSXee5fJ0dEXJpQUo0lqHS8mlEMLbOW02St9fT+GKFGrSD+LTty9Rc28l4vrrsYa2r2RHiPbIr8znL5v/wleZXzGh7wQemvoQcaFxng7rjNJaYz92zNWLV5WaSnnqbqwVxrfwDivUDIklZvxUgkePITApCf/ERJRv917Xzp6VRcGLKyl+8010VRUhl1xCrzvuIGj8uNaf3IO1Nq28Uzs5WHzQKKHM3sK23G2U2ozS3EFhg1wllBNjJtI7sHeHYtFasze7jI/SctiQmsP+XGOs7Tlx4Uw314gb2iekQ8cQwl1nj6HzwZgU5VIgC2NSlJu11mlu2yRqrdPN21cBi7XWE5RSScBq6idF+QxIlElRhBA9XW1pKUVrXqNo1Soc+fn4Dx9Or/nzCJs5UyY6EWfcR0c+4uHvHqbaUc3C8Qu5ZeQtWJSM0wGjbLbm2FF2frOWvd++T2hGHvE5EFRTvzC9//DhBCSNIrBuCYWEhG6R5FUfOEDh8uWUrDcmfQn/6U/pdccC/BO8f9KX9Rnrmy2J+/U5vybQN5AtOVvYlrONopoiAPqH9HclbxNjJhITHNPhGJxOza7MYtcacccKK1EKJg6OYkZSDNOTY+gf0XN7fEX31hXLFlwJPI2xbEGK1voRpdRfgW1a63eVUkuBywA7UAT8v7qETyn1ADAfcAC/01p/eKpjSUInhOjOjIlOXqL4jTfNiU7OJ2r+fIKnni/lNeKMK64u5pHvH+GjIx8xuvdoHr7gYeLDZZ2xU9mRu4Plu5exP/VrRub5Mr0mgeF5vjj3H3TNcOpK8pKTCKxbDP0MJnmVO3ZQ8MIyyr/4AhUYSOT11xF1221n1TqVLU1aUScmOMaVwE2KmUS/kM55bxy1TrYcLjR64tJyyC2twdeqOH9ob2aYa8T1CW37mnNCtJcsLC6EEJ2sKjWNwpQUSjdsqJ/oZP58AkaM8HRo4iz1debXLP52McU1xdx9zt3MT56Pj0UmxGirA0UHWJG6gg8Pf4hSip8OnsVt4dPpc7zUGI+Xmkr1nj04y40xWMrPD/8RI4yePHNBdP+hQzstydNaU/7VVxS8sIyq7duxRkQQeescIm++GZ9I7144vdZZy/Gy4xwsPkh6UTrpxel8cvSTFrf/4OoPiAuN67Qv0arttWw6eJKPUnP4ZG8uxZV2AnwtXDQsmhnJxhpx4YGe77EVZxdJ6IQQohNop7N+opPvv8cSHGws0nvrHJnoRHhMua2cv239G+sOriMxMpFHL3iUEVHyxUJ7ZZVnsTJtJevS11FdW80lAy5hwegFjOkzBu10Yj92jKrU+nXymkvyApOTXGvlnW6Sp+12Sj/8kIJly6k5cACf2Fh6zZtHxLW/wBJ0eotXd3daa/Iq80gvTudg0UHSi9NJL0onoySDmlpjuRCFYkDoAHIrc11t7jprWvnyGgdf7jfWiPtiXx4VtlpCA3y4bGRfpifF8JNhfQj0kzXihOdIQieEEB3gtNkofe99ClakYDt4yJzoZC4R118nE50Ij/o++3sWbVpEbmUu85Pnc/c5d+NnlTGbnaGwupDVe1fz6r5XKbWVMjFmIvOT5zO1X8N1I7XTie3oUaMXz1wrr3rPHpwVFYCxdqH/iOGuUs3GSZ49L4+s399Dv8cepfzLryhcsQL7iRP4JybQ6447CLvyym4xfq+jSmpKOFh8sEHidrD4oGvSEoA+gX1IjEwkISKBxMhEEiMSGRI+hCDfoBbH0C05f0m7ZyIsrrTxyZ5cNqTl8HX6SWwOJ72C/bgiqS8zkmOZEt8LPx8Zeyq6B0nohBCiHWpLSiha8xqFL6+iNv8k/iNGGBOdzJghE50Ij6pyVPH09qdZvW81g8IG8fDUh71+0WxPqbBX8OaBN3lpz0vkVeYxPHI4C0Yv4PJBl7dY0togyUtNre/JaybJqzl4iMotW8DPD2w2AseNo9eddxJy0U9Qlp6XTFQ7qskoyXAlbHUlk3mVea5tQnxDGiRuCREJJEYkEhEQccp9tzbLZVvklVazYU8uG1Jz2JxRQK1T0y88gOnJMcxIimHC4Cisskac6IYkoRNCiNNgy8yi8KWVFL+5Fl1ZSfDUqUTNn0fw+TLRifC8H/J/4IGND3C09Ci3jLyFheMXEugjM+t1NXutnfcz3iclNYUjpUeIC4nj9qTb+XnCzwnwCWj1+drpxHbkaH2pZmoqVWlp6KoqYwOLhbh/PkPopZd28SvpHA6ng+NlxxskbgeLD3Ks7BhO7QTAz+JHfEQ8iRGJJEQmkBCRwLDIYfQN6ntG/5YeK6hkg7nQ945jRWgN8b2DmZEcw4zkGEb3D5e/7aLbk4ROCCHaoOrHVApXpFD60QawWAifdSVR8+cTMHy4p0MTAlutjed+eI6U1BT6BvXloakPMTl2sqfDOus4tZMvjn3B8tTl/HjyR6ICopgzcg43jLiBML+w09pX9pIlFL+5FhwO8PUl4tpriV38YBdF3j5aa3Irc109bXUlkxnFGdicNsAY5zYwbKArcav7PTB0oEcm5tFak55X7lpeYE+2UdaZ1C+MGUlGEpcQHSJJnOhRJKETQogWaKeT8q+/pjBlBZVbtmAJCSHihuuJuvVWfGM6vm6REJ1hf+F+/rTxTxwoOsDVCVdz78R7CfWT8ZuepLVmW+42lv+4nE0nNhHsG8z1w65nzqg5RAdFt/p8e14ehy6/Al1TP9GH8vcn4dNP8OnTpytDb1FJTQkHig40GOt2sOggZfYy1zbRQdEkRiS6SiUTIhOID4/3eC+x1prdmSWuhb4zThrlrecOijTWiEuKYWAv75pURpxdJKETQohGjIlO3qMgZQW2Q4fwiYmpn+gkJMTT4QkBGGVtKakpPPfDc0T4R7BkyhJ+MuAnng5LNLK3YC8rUlew4egGrMrKz4b+jNuTbmdw+OAWn5O95C8Ur10Ldnt94xnqpatyVJFRnNFgcpL0onTyq/Jd24T6hTZI3Op+h/uHd2lsp/L2ziye3LCfE8VV9IsI5J7Lh9E/MtCVxJ0oqcZqUUyJ78X05Bimj+pLdFjr5bBC9ASS0AkhhKm2uNiY6OSVl+snOlkw35joxAtmkhPeI6Mkgz9v/DM/nvyRGYNn8MDkB1qdNEJ41vHS46zcYyx5YHfauWzQZSxIXkBS76Qm22bMvpqaffuatPuPGEH82+s6JR6H08Gx0mNNErfjZcfRGJ/3/K3+xIfHu2aVrBvrdqbHuTVHa02Nw4m91sk7u7J4+P29VDucTbbz87FwYWIfc6HvaCKCZNIq4X0koRNCnPVsmZkUrnyJ4rXmRCcXXECv+fMImjLF4x9ahHDn1E5e2fsKS3csJcAngD9P/jMzhszwdFjiNJysOsnqvatZs28NZfYyJsdOZkHyAs6LPa/B35vGPU73Th/O7HH9T/t4WmtyKnJciVtdqWRGSQZ2p9EDaFEWBoYObJC4JUYkMiB0AFaLFUetE1utE5vD7Xcbb9trndQ0ftxst9U95vY8u9t27s+z1zbervXPpJFBvmz84yUE+5/5sXpCnEmS0AkhzlpVP/5IQUoKZRs+Nic6mUXU/Hky0YnoljLLMlm0aRHbcrdxYdyFLJmyhD5BnhlPJTqu3FbOGwfeYNWeVeRX5TMyaiQLRi/gsoGX8d4POdz/1o9U2Wtd2/v7WLj7oqFMie/VIMmpcUtySmqKya48TH7NUfJtRym0H6PYcQyHrnLtx48oAumPv7MfPo5+WGtjwR6Nw+HTNCEzfzs78eOfj0Xh52PBz8eCr9WCn9WCv3nfz8e472t1u+9jwd/qtr3bdn4+xnMfXr+32WMp4PDj7VuHToieRBI6IcRZRTudlH/1lTHRydatWEJCiLzxBiLnzJGJTkS3pLVmbfpantz6JEop/jjxj8xOmC29x17CVmvjvUPvsSJtBUdLjzIwdCA5xyZTUqnx7/MpyrcYbY+gJn86jtJxxpOUDYt/Lhb/HKzmb0tALhaf+glKdG0guiYGiz0Wa20//JzGT4A1xJUM1SVEdYmVn0/ThMmvmSSq2TZzP/6naPe1WrpkHbepj39OVnFVk/b+EYFsuu+STj+eEN2NJHRCCK9mz8sj6/f30O+Jx6n49lsKV7yILSMDn9hYY6KT666ViU5Et5VXmcfibxezMWsjk2Im8dDUh+gX0s/TYYkuYHc4eG7rO6w+8CIVHEFrcM/ZtbZQWx1DbCScrM52jXPzs/gxMDSe+PChJEQkMiwykRFRw4gNicbSAxcfb4+3d2Y16dEM9LXy2DWj21WmKkRPczoJnRQgCyF6nLx/PE3Vtm0cunIW1NTgP3Ik/Z58krAZ02WiE9Ftaa354PAHPPr9o9hqbdw36T5uGnETFnV2fEA/m5RU2nlzRyavfHeUjJMBRAb/F7rfYpRPeYPtlHLiE5DDuL6XkRA5m2ERw0iITCAuJA6rxeqh6LuHuqStM8YcCuHtpIdOCNGtaa1xnDhB5Y6dVO3cScXWLdjSDxoPWiz0//v/9QW4pwAAIABJREFUEjp9upSqiW6tsLqQh797mE+OfsKYPmN4ZOojp5ziXvRMqVklrNp8lHd+yKLa7uTcQZHcet4gZo6OYcIr44DmPnMpfrxt95kOVQjRzUkPnRCix9I2G9V791K5cydVO3dRtXMnjrw8AFRQEJbQELBYwOkEq5WK77cQNkNmBBTd1xfHvmDJ5iWU2kpZOH4htyfdjo9F/vv1FtX2WtbvzmbVd0fZdbyYQF8rV4+LY855A0nqV7+GW2xwDNkV2U2eHxss43yFEB0j/6MIITzKUVhI1a5dVO3YQeXOXVSnpqJragDwjYsjaPJkAseNJWjcOKwRERyaMdNI5gDsdkreeos+v74bnz4yM6DoXkptpTyx5QnePfQuwyOH8/zlzzM8SmZb9RZHCypY/f0xXt92nKJKO0P7BLPkqlFcc24cYQFNS78Xjl/Ikm+XUF1b7WoLsAawcPzCMxm2EMILSUInhDhjtNNJzcGDrp63qp07sR09ajzo60vgqFFE3nQTgePGETh2LL59oxs8P3vJX9BOZ5N95v/7OWIXP3imXoYQrdp8YjOLNi3iZNVJ7hpzF78a8yt8rTK+s6erdWq+3J/Hqu+O8tWBfCxKMT2pL3POG8SU+F6nLP2eFW9Mtb90x1JyKnKICY5h4fiFrnYhhGgvSeiEEF2mtryC6h93U7ljh5HE/fADzjJjCm5rVBSB48YRcf11BI4bR0BSEhZ//1Pur2rXLrDbGzba7VTt3NlVL0GI01Jpr+Qf2//Bmv1rGBw2mFUzVzG6z2hPhyU66GR5Da9vO84r3x0jq7iKvmH+LLw0kRsnDiQmPKDN+5kVP0sSOCFEp5OETgjRKbTW2LOyXD1vlTt3UbN/v1EeqRT+iYmEXXmlq3zSd+DA057IJP7tdV0UvRAdtzNvJw9sfIDjZceZM3IOC8cvJMCn7R/2RfeitWbHsSJWbT7KBz/mYKt1cv7QXvx51kguG9UXX6vMTipEj7b7dfjsr1CSCeFxcOmDMOZ6T0fVLpLQCSHaxWmzUbNnD5Vu5ZOO/HwALEFBBI49h9Bf/YrA8eMJPGcM1tBQD0csRNeoqa3h2V3P8mLqi/QL6UfK9BQmxkz0dFiinSpqHLyz6wSrvjvK3uxSQv19uHnyQOacN5CEaPk7JoRX2P06vPdbsJuL15ccN+5Dj0zqJKETQrSJo6DA7HkzZp+sTk1F22yAOXnJlPMIGjeOwHHj8E9MRFnP7jWUxNlhT8EeHtj4AAeLD/KLxF9w78R7Cf7/7N13eJRV2sfx78xk0jvpHRJ6DSBNQSyABQhg74ruCrtgxVddXWXVtayu2Na1omvB1dVAsIKiWEBASOiKJCFtUinpbcp5/3gmyYQk1CSTcn+uay6SmWfmOYMhPr+5z7mP0cvZwxKnIL24gvc25fDJtjwq6iwMDvfliXnDmT0yAi83uVwSokdZ90hTmGtgrtHul0AnhOgJlNVKXXpG0/TJ7WmYs3MA0BmNuA8dSsA11+CROEprXhIScpxXFKJnMdvMvLHrDV7b8RoB7gG8fN7LTI6a7OxhiZNkttr4em8R7/6czc+Zh3A16LloeBjXTYxldEyA7G8pRE+jFBz4QavItaYsr3PH004k0AkhsFZWUrtzZ+Pm3TU7dmCrrATA0KcPHomjCLj8CnvzkiHHbV4iRE+WUZrBAz89wJ5De7io70X8Zfxf8HPzO/4TRZdRWFbLB1ty+GBLDsUVdUT6e/B/Fwzk8rHRBHnL7zchehyrGfasgo0vQOFO0OlB2Voe5xfV+WNrBxLohOhllFKY8/KaTZ+s+/33puYlAwbgO/PixumTxuho+ZRaCMBqs/Ler+/xQuoLeBo9+efZ/2R63HRnD0ucIKUUP2cc4t1N2azdW4RNKc4eEMwTE2KZOjAEg15+zwnR49RVwLb/wKZ/Q3keBA2AWS+A3gW+uLv5tEujh9YYpRuSQCdED2err6d2z57Gvd+qt6dhLTkIgN7LC4+RI/FZuFDb+02alwjRqtzyXB7c8CCpxalMjZ7KwxMfJsgjyNnDEiegrMZMcmoe727KJrOkigBPI7dM7ss142KJ6ePp7OEJITpCeT5sfgW2vg11ZRB7Jlz8T+g/HfT2DrUGo3S5FEJ0TZaDBxsrbzVpaVrzEvvebcaYGLwnTdLCW2IibgkJ0rxEiGNQSvG/3//HM1ufwaAz8Pez/s6sfrOkat0N7Mkv471N2axKy6fGbGVUtD//vGwkF48Ix90ov/eE6JGK9sDGl2DX/0BZYUgSTFwMUWNaHjvi8m4b4I4mgU6IbkxrXpLebO83c45D85Jhwwi47jpt77dRo3AJDnbyiIXo2j7P/JznU5+nsKqQYM9g/Fz92F+6nwnhE3j0zEcJ8wpz9hDFMdSarXy5u4B3f84mNacUd6OepJGRXDshluFRss5RiB5JKchcDxtfhIx1YPSEsfNhwkII7Ovs0XUKCXRCdCPWykpqduygxrF5SVUVAIagIDwTRxFw5ZV4JI7CfehQ9K6uTh6xEN3H55mfs3TjUmqttQAUVxdTXF1MUnwSj5z5CHqdbCTdVeUerub9zTl8tDWXw1X19A3y4q8zh3Dp6Cj8PI3OHp4QoiNYzbBnpb3RyS7wCoFz/6qFOc9AZ4+uU0mgE6ILMBcXY7rrbqKWPdtYRVNKYc7Nbdm8RCmtecnAgfjOntXUvCQqSqaBCXEankt9rjHMOdpSuEXCXBdktSl++L2Edzdl892+YnTAtCGhXDchjknxfdBLkxMheqbackhtaHRi0hqdzH4Rhl8ORndnj84pJNAJ0QUcfPnf1GzbRsHfHsEzMZHqtFRq0rZjPXQIAL23t9a8ZNo0be+3kSMxeHs7edTO4TglLswrjNtH387F/S529rBEN6WUYtfBXaSkp1BYVdjqMW3dL5zjcFU9H23N5f3N2eQeriHYx43F5yRw5bgYIvw9nD08IURHKTNpjU62vQ115RB7FsxcBgnTmhqd9FIS6IRwspq9v1L64YegFJXffEPlN99gjI3B+6yzHJqXxEvzElpOiSuoKmDpxqUAEurESSmqKuLTzE9ZnbGaA2UHcDe44+HiQY2lpsWxsm7O+ZRSpOWW8t7P2Xy2q4B6i43xfQO594JBTB8ShqtL776YE6JHK9ytrY/b/bG2d9yQOTBpEUS20uikl5JAJ4QT1eflkX3dddo0SgCDAb/Zs4h44gnnDqyLam1KXK21ludTn5dAJ46r1lLLd7nfkZKews8FP2NTNkaHjObGSTcyPXY63+d93+wDAwB3gzu3j77diaPu3arrLazens+7m7LZk1+Ot5sLV54RzbUTYhkQKlusCNFjKQWZ39kbnXwLRi844xat0UlAnLNH1+VIoBPCSeoyM8m+/gaUvakJAFYr5V98Schdd0lHSgf7Du9jZfrKNqe+FVQVsK1oG4khibLWSTSjlGJHyQ5SMlJYc2ANFeYKwr3CuWX4LSTFJxHjG9N4bMOHAjKl1/kySip5b1M2H2/Lo6LWwqAwHx6bM4w5iZF4u8mlixA9ltUMu5O1IFe0C7xDtf3hxtzU6xqdnAydaqgMdBFjx45VW7dudfYwhOhQtb/+Ss7Nt2CtqgKrFSyWpgeNRvwvvZTwhx9y3gC7gIr6Cr488CXJ+5PZc2gPRr0Rg87QatOKBsEewUyLncb0uOkS7nq5wqpCPsv8jJT0FLLKs/Bw8eD8mPNJSkjijLAz5GejC7JYbXzzaxHvbspmQ/ohjAYdFw4L57qJsYyNDZCmT0L0ZLXl2tq4za/YG50MhEmLtX3iXNycPTqn0Ol025RSY0/kWPmYS4hOVrN9Ozl/vBW9lxeu/v7UZ2Y2P8BspiYtzTmDczKlFNuKtrEyfSVrs9ZSa62lf0B/7j3jXmb2m8mG/A2tTom7b9x9eLh4sCZrDR///jErfltBiEcI58eez4y4GYwKGSUX8L1AjaWGb3O+JSU9hU0Fm1AoxoSOYf6w+UyPm46X0cvZQxStKC6v5YMtuXywJYfC8loi/Ny5Z8ZALh8bTbBP77yQE6LXKMuzNzr5j9boJG4yzHwOEs7v9Y1OToZU6IToRFWbNpH7pz/jEhRE7FvLMUZGOntIXUJJdQkpGSmsSl9Fdnk23kZvLux7IfP6z2Non6HNPpk/XpfLKnMV63PXszZrLT+ZfqLeVk+IRwjT4qYxPXa6hLseRinF9pLtpKSn8FXWV1SZq4j0jmRW/Cxm95tNtG+0s4coWqGUYlPmYd7blM2aPYVYbIopA4K5bkIs5wwMxsUg/0aF6NEKd9kbnXyirZcbOgcmLoLI0c4eWZdxMhU6CXRCdJKK9esx3XY7rrExRL/5JsaQEGcPyaksNgs/5v1I8v5kfjT9iFVZGR0ymksGXMK02Gl4uJx++/HK+kq+z/u+1XA3I24GI4NHSrjrpgoqC1idsZrVGavJqcjBw8WDabHTmJMwhzGhY+S/axdVXmtmZaqJdzdlk15ciZ+HkcvHRnH1+Fj6BkkFVYgeTSmtwcnGF7WGJ0YvGHMDjF8AAbHOHl2XI4FOiC6m/MsvMd3zf7gPGkT066/hEhDg7CE5TVZZFivTV7I6YzUHaw4S5BHE7PjZzE2YS5xfXIedtyHcrclawwbTBi3ceYYwPXY60+OmS7jrBqrN1azLWUdKRgpbCragUJwRdgZJ8UlMi52Gp9HT2UMUbdibX857m7NZlWaiut7KyCg/rp0Qy6yREbgbe9GWLDs/gnWPaNPM/KK0Zg8jLnf2qIToWJZ62NPQ6GS31uhk/AIYexN49N7roeORQCdEF1L6yScU/PUhPEYnEv3KK71yQ/BqczVfZ39N8v5kUotTMegMTI6azLyEeZwVdRZGvbFTx1NZX8n6vKZpmWabuTHczYibwYjgERLuugilFKnFqaSkp7A2e23jlMqkhCRm9ZtFlE+Us4co2lBnsfLV7kLe/TmbrdlHcHPRM3tkBNdOiGVktL+zh9f5dn4En94GZoe9Do0eMOsFCXWiZ6ot0xqdbHoFKvIheJDW6GT4Zb220cnJkEAnRBdx+J13KXr8cbzOPJOol15E73H60wi7C6UUew7tIXl/Ml8e+JJKcyWxvrHMSZhDUnwSwZ5dY1uGhnDXULmTcNc15FfmN06pzK3IxdPFk+lx00mKT2J06Gj5b9JFrEoz8fSafeSX1hDh78E9MwYyNi6AFZtz+PCXXA5V1RPXx5NrJ8Ry6Zgo/D1dnT1k51k2DMpyW97vFw137u788QjRUcryYNO/tUYn9RXQdwpMuk1rdCLdak+YBDohnEwpxaFXX6XkuefxmXY+Ef/8J3rX3nEhU1pbymeZn5Gcnsz+I/txN7gzPW46cxPmMiZ0TJduPV5RX6E1VMle2xjuQj1DmRY7TcJdJ6g2V/NNzjekpKewpXALAOPDxpOUkMR5MefJlMouZlWaifuTd1Fjtjbep9eBTWl/njc4lOsmxHJWQhB6fdf9d98pygvg2UFtPz5+IfSdDLGTZAqa6L4KdmrTKvck2xudzIVJiyAi0dkj65Yk0AnhREopSp59lkOvv4Ff0mzC//53dC49e4cQm7KxqWATK/evZF3OOsw2M0P7DGVe/3lc2PdCfFx9nD3Ek9YY7rLWsiG/KdxNj5vO9NjpEu7aiU3Z2Fa0jZT0FL7O/ppqSzXRPtEkxScxK34WEd4Rzh6iaMOZT36LqbSmxf0+bi58decUIv17z4yENhX/ql3g7vwIbObWjzG4aVULSy2gg/ARWuv2vlMgZiK4+3bqkIU4KUpBxjp7o5P1TY1OJiwE/xhnj65ba/dAp9PpLgCeBwzAG0qpJ496/C7gFsAClADzlVLZ9seswC77oTlKqdnHOpcEOtGdKZuNosce48iKD/C/8grCHnoIXQ/eR6WgsoBV6atYlb6K/Kp8/Nz8mNlvJnMT5jIwcKCzh9duWgt3YV5hTZW7oBFduvLYFeVV5PFpxqekZKRgqjThZfRiRtwMkuKTSAxJlL/PLqysxsxXuwu495NdrT6uAw48eXGrj/UKSsGBH7QL3PSvwcUDEq+FwDj49rHW19ANSYK8rZD1Ixz4EfK2gLUedAaIGGUPeJMhegK49b512KILstRrWw5sfBGK94B3GExYAGNulCpzO2nXQKfT6QzA78A0IA/4BbhKKbXX4ZhzgM1KqWqdTrcQmKqUusL+WKVS6oR/+0igE92VslgoeOBBylJSCLx5PiFLlvTIi9J6az3f5X5H8v5kfs7/GYViQvgELul/CefEnIOboWcvdG4Id2uy1rAxf2NjuGvolinhrm3V5mrWZq8lJT2FrUVb0aFjfHjTlMr22KpCdIx6i43vfy9hVZqJr38tot5iw6DXYbW1vIaI9Pdgw33nOmGUTmY1w94U2PgCFOwAr2AYdyuccTN4BmrHnGiXS3MN5G5pCnimrWCzgN4FIsc4BLzxWigUorPUlGqNTja/AhUFEDJEa3Qy7FJw6R1LSzpLewe6icBSpdQM+/f3Ayilnmjj+ETgJaXUmfbvJdCJHs9WX0/+3Uuo+Pprgm5bTNDChT3uon7/kf0k70/ms8zPKK0rJcwrjDkJc5iTMIdI7965QXp5fXmzyp3FZiHcK7yxcjc8aHiP+zk4WTZlY2vhVlIytCmVNZYaYn1jG6dUhnmFOXuIog1KKVJzSlmVZuKznfkcqTYT6OXKrBHhzB0dxYGSSv6ycnezNXQeRgNPzBvOnMRe9DuhrgJS39GaQJTlQp/+2rqhEVeC0b19zlFfBTmbmgJefhooKxhcIeqMpoAXdYZ0DxQdozRXC3GNjU7Otjc6OU8anXSQ9g50lwIXKKVusX9/HTBeKbWojeNfAgqVUo/Zv7cA29GmYz6plFrVynP+CPwRICYmZkx2dvaJjF2ILsFWU0PebbdT9eOPhN5/H4E33ODsIbWbyvpKvsz6kpX7V7Lr4C5c9C6cG30u8/rPY0L4BAz6XrR/1HE0hLuGyl1DuGuo3PW2cJdbnsvqzNWsTl9NflU+3kZvZsTNYE7CHEYGj+xVfxfdzYGDVaxKM7Fqu4nsQ9W4ueiZNiSUeaMjmdw/GKOhaRp5a10ue02YKy/QLnC3vgV1ZRAzSatUDLgAOnqqfW25PeD9oAW8wp2gbODiDtHjtIAXN1mr5knVRJyOgh3atMrdydr3w+bBxEXaVGDRodo70F0GzDgq0I1TSi1u5dhrgUXA2UqpOvt9EUqpfJ1O1w/4FjhPKZXR1vmkQie6E2tlJXkLFlK9bRthj/yNgMsuc/aQTptSirTiNJL3J7M2ey01lhoS/BOYmzCXmfEzCXQPdPYQu7zy+nK+y/mOtdlrG8NdhFcE02Kn9ehwV2WuYm3WWlalryK1OBUdOiZGTCQpPolzY87F3aWdqhWi3R2qrOPzXQUkp5rYnluKTgcT+/VhbmIkFwwLw8e9c/eK7NKK9sLPL2nTJ5UVBs/WglzUCV13dYyaUsje2FTBK7KvbzR6atMy+06GuClat0FDz27SJdqBUpC+Tps+fOB7cPXW1saNXwD+0c4eXa/hlCmXOp3ufOBFtDBX3MZrvQ18ppT6uK3zSaAT3YW1tJScP/yR2l9/JeKpJ/G7uHs3AThYc5DVGatZuX8lWeVZeLp4cmHfC5nXf16PDSCdoSHcrclaw88FPzeGu4ZumcOChnXrv1ubsrGlcAsp6Smsy1lHjaWGON84khKSmNlvpkyp7MJqzVa++bWIlakmvv+9BItNMTDUh7mjI0kaFUG4n6zNaqSUdmG78UVI/0YLSonXap38Avs5e3QtVR+GrJ+aAl7Jr9r9rt5a58y+9gpe+EiQmRaigaUedn9sb3SyF3zCtRA35kbw8Hf26Hqd9g50LmhNUc4DTGhNUa5WSu1xOCYR+BhtauZ+h/sDgGqlVJ1OpwsCfgaSHBuqHE0CnegOLCUl5My/mfrsbCKfew6fc89x9pBOicVmYYNpA5/s/4Qf8n7AqqwkhiQyr/88psdOl32/2llZXRnf5X7H2qy13T7c5ZTnkJKRwqcZn1JQVYCP0YcL+l5AUkKSNIbpwmw2xaYDh1iZauLL3YVU1lkI9XVjzqhI5iRGMjhcWuQ3YzXDnlVapaJwZ+uNTrqDyhLI/kkLd1k/wsHftfvd/LS97xoCXuiwjp8uKrqemlLY9hZsftXe6GSovdHJJTJl14k6YtuCi4Dn0LYtWK6U+rtOp3sE2KqUWq3T6b4BhgMF9qfkKKVm63S6ScCrgA3QA88ppd481rkk0ImuzpyfT85N8zEXFxP98r/wmjjR2UM6aTnlOaxMX0lKegolNSUEugeSFJ/EnP5z6OfXBT9t7oGahbv8n7EoC5HekY1r7ob2GdrlQlFFfQVrs9aSkpFCWnEaep2eiRETmRM/h6nRU2VKZRe2r7CC5LQ8Vm/Pp6CsFi9XAxcOD2duYiQT+vXB0Ns3/j5aXYXW/GHTv6E8D4IGaOuGRlzRfo1OnKmiUKvgHfhBC3iHM7X7PQIg9kxtD7y4yRAyWBpe9GSlOdrPeOo7UF8J/aZqQS5eGp10BbKxuBAdpD4ri+z587FVVBL96qt4jk509pBOWI2lhm+yvyF5fzJbi7ai1+k5K/Is5vWfx5SoKRj1skbGWRrC3ZqsNWzK39Slwp3VZmVz4WZS0lP4Nudbaq219PPr1zilMsQzxCnjEsdXVF5LynYTK9Py+bWgHINex9kDgpmTGMm0waF4uMpUuxbK8+2NTt7WGp3Enqld4Paf0bMrV2V59oD3o9ZopTRHu98zCOLOalqDF9RfLvR7gvzt2rTKPSu174ddov2ch49w7rhEMxLohOgAtft+J+fmm8FqJebNN3AfMsTZQzoupRR7D+9l5f6VfJH5BRXmCqJ9opmbMJfZ8bMJ9Qp19hDFUcrqyvg251vWZq9tHu7ipjMjdgZD+gw5qXB3ql0Is8qyWJ2xmtUZqymqLsLH1YeL+l5EUnxSt5oa2ttU1ln4anchq9JMbMg4iFIwMtqfuaMimDkygiBvaWnfqqI9sPEl2PU/rdHJkCSYuBiixjh7ZM5xJLtp/V3Wj1Bu0u73DnMIeJO19YPyu6B7UEpb/7nxBa0y6+oDY26QRiddmAQ6IdpZza5d5N7yB3RubsS8tRy3+HhnD+mYyurK+CzzM1buX8m+I/twM7gxLXYa8/rPY0zoGPS6HvxJcw/SEO7WZK9hc/7mkw53q9JM3J+864T3CSuvL2dN1hpS0lPYUbIDvU7PmRFnkpSQxNToqT1+0/juymK18eP+g6xMM7F2byG1ZhvRgR7MHRVJUmIk8cEnvBVs76IUZK7XKhUZ6+yNTq6zNzrp6+zRdR1KaVMyHQNeZZH2mG9k0x54cZMhINa5Y+3tWtu4fkiS9kHFxpe05jg+ETDB3ujE3c/ZIxbHIIFOiHZU/csv5C5YiCEggJi3luMa3TU/yWroNpi8P5l12euot9UzOHAw8/rP46J+F+HrKs0OurO2wt2MuBlMj5vOkMCW4W7iE+soKKtt8VqR/h5suO9cQJtSualgkzalMvdb6qx1JPgnkBSfxMX9LibYM7hT3p84OUopduaVsdK+6ffBynr8PIzMHKGtixsTGyBV1LZYzdpUs40vQOEu8AqB8bfC2Pndq9GJsygFB/c37YGX9RNUH9Qe84/RpmY2BDy/XrInYVew8yP49DYw1zTdpzeC0QPqyrVGJ2feBkPnSaOTbkICnRDtpPLHH8lbfBvGiAhi3lqOMbTrTVEsrCpkVfoqVqWvwlRpwsfVh5n9ZjKv/zwGBQ5y9vBEB2gMd1lr2Fyghbsg9wjiPSfhZR7NwcPBZBRXUVhei4tvGm7Ba9AZS1Fmf+pKZmApT+SGKR6UGX5me+k6DtWW4Ofm1zil8mSndYrOk3u4mlVpJlZuN5FZUoWrQc95g0OYkxjJ1IHBuLnIurg21ZZDakOjE5PW6GTSYhh+ec9odOIsSkHxr/YK3g+QvQFqjmiPBfazV/CmaFM1fWQbk3ahlBbc6iq0ZiZ15fD+ZVBV0vJYFze48gOIP1emx3YzEuiEaAfla9ZiWrIEt4QEYt58A5fArvPJrdlqZn3eej7Z/wk/5/+MTdkYHzaeef3ncV7seTI1rgey2RSm0hrSiyubbiWV7D9YRI3LDlx8d2HwSkens+FiCyLKOIGsEgvKfx06vbnxdZTNgLL4oXc9jFJ6rJUD8KyfwIjAiYyIDGJ4pB8jovwI8ZUL3K6itLqez3cVsCrNxC9Z2oXyuL6BzE2M5KJh4fh5SkOjYyozaY1Otr2tXfjGnqVVKhKm9exGJ85is0HR7qYpmtkbtQYzoIXohimasWeBdy+bAWCpt4ewCu3PugqosweyxnBW0fLW7P5y7TnKevzzAaCDpaUd+rZEx5BAJ8RpKl21ioK/PIDHiBFEv/YqBt+uMV0xozSD5P3JfJb5GYdrDxPiGcKchDnMSZhDtE/XnAoqTk69xUb2oapmoS29uJKMkkpqzbbG4/p4uRIf4k1CiDcJwdqfIf4W9pRuZG32WjYXbMbaxv/w9RhYnHg7fd0nk1NiYFdeGbtMZWSUVGKz/y8hxMeN4ZF+DI/y0/6MlJDXmeosVr77rZiVaSa++62EequN+GAv5o2OImlUBFEBskfkcRXuhp8bGp3YYMgcmLQIIntpoxNnsVm1Pfwa1t9lb9QCCkDwYC3c9Z2idRQ9espra2vCRlze+eNvK2g1C1v2oNXs/nKH0FYB1roTO6erN7j5aDfHr918we3ox3y1rz+9rfUKnV803Lm7ff9ORKeQQCfEaTi8YgVFjzyK54QJRP/rJfReXk4dT5W5iq8OfEVyejI7S3bionfhnOhzmJswl0kRkzDoZYpVd1RVZyGjpLJFxS37UDVWW9Pv5Uh/D+JDvOnfEN7sAS7A69hrII7UHmHKh1NafUyHjp037Gx1THsLytmVV8Zukxby0ksqUQ4hb0SUH8MiJeR1BJse8kzLAAAgAElEQVRNsTX7CCvTTHy+M5/yWgtB3m7MHhnBvNGRDI3wlamwx6MUZH5nb3TyLRi9YLS90UlAnLNHJwCsFijY3rQHXs4mMFcDOm1j84b1d5XFsOa+5mvCjB4w64XjhzqltNc8Zgg7Kmy1WjmrAHPVib0vF4+jwpaPQxA71v2+zYObq/epVY5bW0N3on9fokuSQCfEKTr0xhsUP/NPvM85h8jnlqF36/ipi59nfs7zqc9TWFVImFcYt4++nYv6XsSOkh0k70/mq6yvqLHU0M+vH/P6z2Nmv5n08ejT4eMS7eNQZV2zSlt6cSUZxZXkOzQrcdHriO3j2RTYQrxJCPahX7AXXm4up3zu6R9Pp6CqoMX94V7hrL107Qm9hmPI22VqquQ1/K8j1Fer5A2zT9UcFulHiI+EvJORXlzJqjQTq7abyDtSg4fRwIyhocwdHcWZ8X1wMci0wOOymmF3shbkinaBd6jW6GTMTdLopKuz1EN+atMeeLlbwNKymVMjN18YdU1TVaxZ9ayyKZgpW9uv0UDv0rz61awa1krYOvrmeL+hC0x97goVTdFuJNAJcZKUUpQ8/zyHXnkV34suJOKpp9AZO/6X8+eZn7N041JqrQ4X9zoX/N39OVhzEA8XDy7seyFzE+YyMnikfDrfRSmlyC+rZX9RReP0yIbwdqS6af2ah9FAfIhX4xTJhltsHy+MHXDR3trPl7vBnaWTlnJxv4tP+XUbQt5Oh0pey5Dnb5+y6SshrxUlFXV8uiOflWkmdpnK0OvgzIQg5o2OZPqQsNMK8r1Ki0YnA7VGJyMu15pBiO7HXAumrfD2MX5HNQStEwpbvrRZJXNxk0YhosuSQCfESVBKUfTEExx55138Lr2E8L/9DZ2hc6YxtlVBMeqNPDjhQWbEzcDL6Nwpn6KJ2Woj+1B1i9CWUVJJdX3TerUAT2NjWIt3CG8Rfh7o9Z178dBaBfh0wlxbKuss7M0v16p4eaXsMpWRebCqMeSF+bo3TtVsqOQF+/SuC+7qegtf7y0iOdXET+kHsdoUQyN8mZsYyeyRETJ99WSU5dkbnfxHq9LETYZJt0HC+dLopKdYNgzKclve7xcFd+7p/PEI0ckk0AlxgpTVSsHDD1P28ScE3nA9Iffd16lVsBH/GYGi5b/BttY4ic5RXW8hs6Sqxfq2rINVWBzWt0X4uTc1JnFoTtLHu3cFlbY0hLydeaWNlbzWQt4Ie+OVnhjyrDbFhvSDrEoz8dWeQqrrrUT6e5A0KoI5iZEMCPVx9hC7l8Jd2rTK3Z9o66SGzoGJiyBytLNHJtqbrAkTvdzJBDqZ0yF6LWU2k3/vvZR/8SVBf1pI0OLFnRrmCioLcNG7YLaZWzwW5iV79XSGI1X1zda2NdxMpU0XEAa9jthAT+JDvJk2JLQxtMWHeOMt0+KOydvNhXF9AxnXt2kNU2WdhT32cLfbVMZOUxnrfitqDHnhfk2VvIYOm0HdLCArpdhbUM7KVBOrd+RTXFGHj7sLs0dqIW5cXGCnV2q7NaW0BicbX9Qanhi94Iw/2BudxDp7dKKjNIQ2WRMmxHFJhU70Sra6Oky330Hl+vWE3LOEPjff3Knn32jayL0/3kuNuQYbtmahrj3WOIkmSikKy2tJL65kf5HDNgDFlRyqqm88zt2op19Q87VtCSHexPXxwtVFpnB1JMeQ13A74FDJawh5IyL9GNaFQ15+aQ2rtptYlWbi96JKjAYdUweGMDcxknMHheBulI60J8VSD3saGp3stjc6WQBjbwKPAGePTgghOpRMuRTiGGxVVeT+eRHVmzcT9vBDBFx5ZeedW9l4bedrvLz9ZRICElg2dRm7D+7ulDVOPcWqNBNPr9lHfmkNEf4e3DNjIHMSI7FYbeQcrm6+d1txJRklVVTWWRqf7+dhbDY9suEW6d/569tE2ypqzezJL2+cqrkrT5uu2SDCoZLnzJBXXmvmy10FrEwzsfnAYZSCMbEBzEmMZObw8ONuLyFaUVumrY3b9G+oyIfgQVqjk+GXSaMTIUSvIYFOiDZYy8rI/eOt1OzeTcQTj+M3e3annbusroz7fryPn0w/MavfLP468a94uHh02vl7glVpJu5L3tlsg229Ttsf7XCVmXpr0/1hvu5NjUkcAlyQt6t0C+2mGkJewxYKu02thzzHvfI6Yj1jvcXG97+XsCrNxNe/FlFvsdE3yIs5oyKZkxhBbB9pZHRKyvK0ELftP1rb+b5TmhqdyL9ZIUQvI2vohGiF5dAhcm75A3Xp6UQ+twzfadM67dx7Du3h7vV3U1RdxIPjH+TygZdLqDiOsmoz6SUVzda2/bD/YLNNtwFsCkqrzdx0VhwJwd70D/UhPtgLH/cusCeQaFc+7kYm9OvDhH5N+zCW15rZY3Ko5JnKWLu3qPHxCD/3xrV4pxPylFKk5pSyKs3EZzvzOVJtJtDLlavOiGbu6ChGRvnJv+lTVbATfn7JodHJXJi0CCISnT0yIYToFiTQiV7BXFhIzvybMefnE/3yy3hPPqvTzv3J75/w+ObHCfQI5J0L3mF48PBOO3dXp5SiqNy+8XZxhUODkioOVtY1HufqoqdfkFeLMNegzmLj/gsHd9awRRfi625kYnwfJsa3HvJ22it5a/Y0hbxIfw+GRfraG69o++UF2qdGHj2l98ZJcVTWWVi13UT2oWrcXPRMGxLKvNGRTO4f3CH7B/YKjY1OXoDM9Vqjk3F/1Bqd+Mc4e3RCCNGtyJRL0ePV5+aSc+NNWEtLiX7l33iecUannLfWUsvjmx9nZfpKJoZP5KkpTxHg3jsX8lttilyH9W0NzUkyiyupcFjf5uPu0ur6tqgATwx6HWc++W2zDpQNIv092HDfuZ35lrqPnR9Jlzi0kLfb1LARuhb2DjhM14z096CPl5G9BRXNtqZoMCm+D3MTI7lgWJhUf0+HpV6rxG18EYr3gHcYTFgAY26URidCCOFAplwKYVeXnk7OTfNR9fXEvP0WHsM7pzqWW5HLXevv4rfDv7Fg5AIWjFiAQd/zO9zVmq0cOFjVbO+2jOJKMg9WUW9pWt8W4uNGQog3c0dHNgtwwT5ux5y2ds+MgdyfvIsac9Mm3h5GA/fMGNih76vbOnofp7Jc7XvodaHO193IpPggJsUHNd5XVmNmT759+4S8Mr7cXdhqFTjM140Vf5jQmcPteWrLYOtb2mbgFQUQPBiSXrY3OpHGMUIIcTok0Ikeq2bPHnJv+QMYDMS8+w7uAwZ0ynm/z/2e+3+6Hx06/nXev5gSNaVTztuZymvNjaEtwyG85R6upuF6WKeD6ABPEkK8OXtAcOMG3PHB3vh5nFqFY05iJECrXS4FUFMKxb9qlY/iXyH1XbDWNT/GXANf3KNV68JGgJu3c8baBfh5NA95fe/7vNXjisrrWr1fnIDSXC3ENTY6ORtmvwQJ50mjEyGEaCcS6ESPVJ2aSu4fb0Xv60Ps8uW4xsV1+DmtNiv/2v4vXt/1OoMDB/Ps1GeJ8onq8PN2FKUUJRV1zbYBaLgVVzisbzPo6RvkxbAIP5JGNVXc+gV7dci+W3MSIyXAmWugZJ89vO21336FclPTMW6+LcNcg9pSeOtCQAdB/SF8lNaAImJUrw55Ef4erU7pjfCXbrQnrWAHbLQ3OgEYNg8mLtJ+xoQQQrQrCXSix6nauJHcPy/CGBJCzFvLMUZEdPg5D9ce5t4f7mVTwSYu6X8J94+/HzdD99gvyWpT5B2pbhbYGgJcRW3T+jZvNxfiQ7yZ3D+42fq26AAPXKQxRMewWeFwphbYivY2hbfDmaDsU1gNrhA0EOLOgpDBEDJU+9MvCp4brk2zPJpPBMxcBgXbIX87ZP0Iuz6yP6iDoAHahXdD0Asb3itCnkzpPQVHr9EcNk/7mTrwPbh6axuBT1gI/tHOHqkQQvRY0hRF9CgV69ZhuuNOXPv2JebNN3AJDu7wc+4o2cHd6+/mSO0RHpzwIHP7z+3wc56KOouVrINNwW1/sbYlwIGDVdQ5rG8L8nYjIcTLYW2bDwkh3oT6Hnt9mzgNSkF5flNgawhvJfscqmw6COynhbVQe2gLGQKB8WBo47O5o9fQARg9YNYLLdfQVRRqF+IF2yE/Tfu6srDp3A0hLyJRC3o9NOS1tXG9aEVrP18Abn4w+S57oxN/pwxNCCG6O9lYXPRKZZ99Tv699+I+dCgxr72Kwb9jLySUUvx333/5xy//INQzlGVTlzG4j/Nb51fUmskoqWpWccsoqST7UFWz9W1RAR4tuknGB3vj7ykNCjpU9eGWUyWL92pNIxr4hDcFtpAhEDpEq8K5ep78+U6ny2V5QVMVryHoVTa0/9dB8EB7Fc9ezQsfAa6yqXaPZLVAWQ4cyoRD6dot7R2wtDKt1y8K7tzT+WMUQogeRAKd6HWO/O9/FD70MJ5jxxL1739j8O7Yi8pqczWPbHqEzzM/Z0rUFB4/63H83Pw69JyOlFIcrKxv1kmyIbwVltc2Hmc06Ijr49UitMUHe+Ph2vO7bjpVfTUc3Nd8qmTxr1qHvwZuflpYcwxvIYPBM9B54z4ex5CXn6Z93RDydHqtktdsTd5wCXndRUOl+HCGPbRl2G/pcCQLbOamY918oa68jRfSwdLSzhixEEL0WLJtgehVDr39NsVPPoXXlMlEPf88eo+ObWCQVZbFnevvJKM0g8WJi7ll+C3odae+huxYU7xsNoWptKbV9W1lNU0XV16uBuJDvJkU36exm2RCiDcxgZ6y8XFHs1q0C+CGwFZk7zB5OBOwf2BmcNOqWf2mNg9vvhHdr9Ofb7h2G3hh032NIc8+VTPjW9j5X+2xhpDXMFVTQp5zKaVViQ+lOwS3dK3ydjgDzNVNx7q426f5DoLBM7XpvX0StJtXUNtrNP26bzMoIYTojqRCJ7otpRQHX36Zgy++hM/06UQ+8zQ6146dLvhN9jc8uOFBXPWuPDnlSSZFTDqt11uVZmrRhMFFr2NEpB+1FhuZByupNTetb+vj5doU2BymS4b7ucv6to6mlDZt0XFbgKK9WhXOWq8do9NrF70NoS3UHtwC+ra9zq0nUkqrRB69Jq+qWHtcp9emkB69Ju9UppSK1tWW2wOb/eYY3hyn9+pdwD/WHtTi7bcE7efYNxL0x/hA6GTWaAohhDgpMuVS9HhKKYqffobDy5fjN2cO4Y89is6l4y6YLTYLz6c+z9t73mZ40HCenfosYV5hp/WaSinG/X0dJZUt16AYdDrO6h/UbKpkQrA3AV6yvq1TVB9uqrQ5Tpd0nGLmG9nKOrcB2gWtaMkx5DVM1cxPg6oS7fHGkJfYtCZPQt6xmWvhyAGHKptDeGtc62jnF61V2xoqbA3BzT8GDKe2LyRwems0hRBCtEkCnejRlM1G4d8eofTDDwm4+mpCH3wA3bE+RT5NB2sOsuT7JWwr2saVA6/knjPuwdVw6sGqotZMyvZ8VmzOYW9B62tQdMCBJy8+5XOIE1RfBSW/NVXbGsKb48Wwu79DV8mGbQEGgUeA88bdUzSs2Tp6TZ5jyAse1DRVMyIRQof1rpBntUBp9lFVNntwK8ulcVovgFdIU5XNcXpkYF/5oEEIIboZWUMneixlsZB//18o//RT+vzhFoLvuqtDpxpuK9rGku+XUFlfyROTn2Bmv5mn/Fo780pZsTmH1Tvyqa63MjjcFz8PY7O1cA1kI+N2ZjVrF8ANUyUb1rodyaLxgtjFXQsP8ec5NCoZCj5h3W+dW3eh04FfpHYbZP8Ao1nIs0/VTP8adqywP8cx5Nmred095NlsUJHf1IDEMbwdyQJb036QuPlpgS1mAvS5xh7Y+mn3uXdeYyYhhBBdhwQ60W3Y6usx3XUXld+sI/iOOwhacGuHnUspxTt732HZtmVE+UTx6rRXGRAw4KRfp7LOQsp2Ex9syWG3qRwPo4HZIyO4anwMI6P8SNmeLxsZn6xjTfFSSqtaHN1Z8uDvDuvcDNrFb/hIGHmVwzq3ONBL50+nazPkmRzW5G2H/WsdQp5BC3nNNkMf1rWqUkpB1cGjqmwN4S0TLA7r0Fw8tJ/RkCEweHbT9Mg+CeDZRz5gEEII0YxMuRTdgq26mrxFi6nauJHQv/yFwOuv67BzVdZX8tDGh/g6+2vOjzmfR898FG/Xk9tAeVdeGSu2ZJOyXavGDQrz4ZrxMSQlRuLr3ny9imxkfBJaa8KgN0L0BLDWQvFvUF/R9JhfdMstAYIGgNG988cu2pdjyGtck7cdqg9qjzuGvMbGK50Q8mrLmkLa0Wvb6o5qRhIQ5zAtsl/T2jafiGM3IxFCCNHjyRo60aNYKyrIXbCQmrQ0wh99BP9LLumwc6UfSefO9XeSW5HLHaPv4IahN5zwlM7KOgurt+ezYkt2YzVu1shwrhoXw6hof+lCeSrMNQ4XxhnwwzNgrmp5nE4PMZOaT5UMGSRT0Hqbhk6kR2+GXn1Ie1xn0H4+HDdDPzrknUiTj8afywyH9v/2rxvW/2kn1D5UcOwe2RDe/GN7V+dTIYQQJ0UCnegxLEeOkHvLH6jdt4/Ip/+B74UXHv9Jp+iLzC9Y+vNSPF08efrspzkj7IwTet5uUxnvb85h9XYTVcepxolWWM1wJPuoPbHsF8jleSf4IrKRsWhDs5CX1hT0Wgt5ygq7k8Hq0HnW4ApDkrSNtA+la0Hu6L3XvEPtTUjim3eQDOgr1WAhhBCnRJqiiB7BXFxMzvz5mHNyiXrpRXymTu2Y81jNPL31aT747QNGh4zm6bOfJsQz5JjPqayz8OkOrVPlLlMZ7kY9s0ZEcPV4qca1ymbTwlljRcNhOtqRbO1CuoG7v3YxHHdm08Vxw8XyyxNlI2NxcnQ68I/WboNnafc1rLV0XJO37wuoOdzy+dZ62PU/rdrbJwFiJzmEN/vPprtv574nIYQQwoEEOtEl1eeZyJk/H8vBg0S/9hpeE8Z3yHkKqwpZ8v0SdpTs4Poh13PHmDsw6tuuqu02lbFiSw4paU3VuEeShpI0KhI/j15ejVNKm27WrOGDPbwdzgRLbdOxRk/tYjhsBAydd1TTh8C2z3HeQ61vZHzeQx33vkTPo9Np+6/5x8CQ2dp9SsHfAmi2DUDTE+DebGlGIoQQokuSQCe6nLrMA+TMn4+tuprY5W/iMWpUh5xnU8Em7v3hXmottTxz9jPMiJvR6nFVDdW4LTnszNOqcTNHRHDVuBhGx/TCalxNafM1Q45/OjYk0Ru1/a/6JED8uc03NPYJP7WL44a1TLKRsWhvOp3289RWBbi3/TsXQgjRbUigE11K7b595My/GZQi9p3/4D5oULufw6ZsLN+9nBfTXiTON45lFyyjn1+/FsftyS9jxeYcUrbnU1lnYWCoD3+bPZQ5ib2gGldf3TQt8ujw1tBFEAB7paNPPESPszd8sE9F84vumKYPIy6XACc6hlSAhRBCdEMS6ESXUbNjBzl/+CN6T09ili/HrV/fdj9HeX05D/z0AOtz13Nh3IUsnbQUT2PThsRVdRY+26mtjduRV4abi1aNu3p8D6zGWeqhNPuo6ZENzUhMzY/1DtPC2qCLm6ZHBsZrbdel6YPoKaQCLIQQohuSQCe6hKrNW8hbuBBDnz7EvPUWrlHtvw/bb4d/487v7qSwqpD7xt3H1YOubgxoe/LL+GBLDqvStGrcgFBvls4awtzEKPw8u3E1rrEZiUPnyIbwVprTshlJUH+Im2yfGtmvqcW6m4/z3oMQnUkqwEIIIboZCXTC6Sq//568227HGB1FzJvLMYYeu8PkqViVvorHNj2Gn5sfb13wFqNCRlFd37A2LpcduaW4uei5eEQ414yPYXRMQPepxikFlcUOFTaH8HY4s3kLdqOXFtQiRsGwS5qvaztWMxIhhBBCCNElSaATTlX+1VeY7vk/3Pv3J/rNN3AJCGjX16+z1vHE5if4ZP8njAsbxz+m/IOiI0b+umo3q9JMVNRZ6B/izcOzhjDPWdW4E9nIGKDmCBzKPGp6ZLp2X4tmJPbqWv/zHda1JYBPmDR3EEIIIYToQU4o0Ol0uguA5wED8IZS6smjHr8LuAWwACXAfKVUtv2xG4AH7Yc+ppT6TzuNXXRzpckrKXjwQTxGjSL61Vcw+LTvtD5TpYm71t/F3kN7uX7wTUTr5nHz8t/YnluKq4uemcPDuXp8DGNinViN2/lR8yYMZbmwehHk/aJtVty4b1tG00bIADq91owkMB6iJzTtidUnQWtGojc45/0IIYQQQohOpVOqtT13HA7Q6QzA78A0IA/4BbhKKbXX4ZhzgM1KqWqdTrcQmKqUukKn0wUCW4GxaJv7bAPGKKWOtHW+sWPHqq1bt57m2xJd3eH33qfoscfwmjSRqJdeQu/pefwnnYSfTD9x34/3YbZaGe52K5t3hVNRZyEhxJurx8Uwb3Qk/p6u7XrOU7JsWOtt0hv4hDetY3OcHhkQBy5unTZMIYQQQgjReXQ63Tal1NgTOfZEKnTjgHSlVKb9xf8LJAGNgU4p9Z3D8ZuAa+1fzwC+Vkodtj/3a+AC4IMTGZzomQ6++holy5bhfd55RD77T/Ru7RdMbMrGi6kv88bu13C1RnD4wFX8oEK4eHgoV4+PYawzq3GtKctr4wEd3J8Hbt6dOhwhhBBCCNG9nEigiwQcSwh5wPhjHH8z8OUxntuifaFOp/sj8EeAmJiYExiS6I6UUpQ8u4xDr7+O78yZRDzxODpj+61Z+yUnl/t/vJ8iyw7MpaMJtlzDn2b055KuUo07WsZ3bT/mFyVhTgghhBBCHNeJBLrWyhmtztPU6XTXok2vPPtknquUeg14DbQplycwJtHNKJuNor8/zpH338f/8ssJe/ghdIbTX+dVU2/ls535vLX1B7JdXkFnqGCw600smXUD4/oGdq1qnKO09+DT28EnAmoOgaW26THZyFgIIYQQQpygEwl0eUC0w/dRQP7RB+l0uvOBB4CzlVJ1Ds+detRz15/KQEX3pSwWCv76EGUrVxJ4002E/N89px209hVW8MGWHD5JzaXWfSPuYZ/iZwzgmSlvMSk6sZ1G3gGUgu8ehx/+Af3Ogcvfgd+/ko2MhRBCCCHEKTmRpiguaE1RzgNMaE1RrlZK7XE4JhH4GLhAKbXf4f5AtEYoo+13paI1RTnc1vmkKUrPourrMd3zf1SsWUPQokUE/flPpxzmas1WPttZwAdbctiWfQRXo4XYAV9SaNvApIhJPDX5Kfzd/dv5HbQjSx2sXgw7P4TEa2Hmc2DoxpuWCyGEEEKIDtGuTVGUUhadTrcIWIO2bcFypdQenU73CLBVKbUaeBrwBv5nv1jPUUrNVkod1ul0j6KFQIBHjhXmRM9hLi7GdMed4GqkZtNmQu69lz433XhKr/V7UQUrNueQnJpHea2FfsFeLJruz0/l/ySzLJ2FIxdy64hbMXTlVv01R+DD6yDrRzj3QZi8RPaDE0IIIYQQp+2E9qFTSn0BfHHUfQ85fH3+MZ67HFh+qgMU3VPJCy9Sk5oKQNjf/kbAFSc3hbDWbOVzezVua/YRXA16LhgWxtXjY6g27OCBDUsw6A3867x/MTlqcke8hfZzJAvev0z7c97rMp1SCCGEEEK0mxMKdEKcjLqsLMo++UT7xmjE59xzTvi5+4sqeN+xGhfkxQMXDeaSMVH4euh5Ke0l3tz9JkP6DOHZqc8S6d2iaWrXkrcNPrgCrGa4biXEneXsEQkhhBBCiB5EAp1oV8piIeem+VrzD7uSl/9N+MNtd22sNVv5YpdWjfslq6kad9W4GCb00zpVHqo5xIKv72Vz4WYuHXAp9427DzdDF99Y+7fP4eObwTsEbvwYggc4e0RCCCGEEKKHkUAn2lXBQw9jKShousNspiw5meA/LcQlOLjZsenFFazYnMsnqXmU1ZjpG+TFXy4axCWjo+jj3RTWthdv5+71d1NWX8ajZz7KnIQ5nfV2Tt2mV+Cr+yByNFz1IXgHH/85QgghhBBCnCQJdKLdHH73PcqSk0GvB5ut8X5lszVW6WrNVr7cXcAHm3PZknUYo0HHBcPCuWpcNBP79WnWAVMpxYrfVvDML88Q5hXGexe9x6DAQc54ayfOZoU1D8Dmf8OgmdqaOVdPZ49KCCGEEEL0UBLoRLuo/P57ip54Ar23N7bKyuYPms2U/rKN1z/dS3JaHqXVbVfjGlSbq1m6cSlfZn3J1Kip/H3y3/F19e2kd3OK6qsh+Q/w22cw4U8w/THoyp03hRBCCCFEtyeBTpy22n37MN15F26DBrLrnn9w3xfp1JitjY/rdWBTYNyUxYyhWqfKo6txjjLLMrnzuzvJKs/i9tG3M3/YfPQ6fWe9nVNTWQwrroD8NLjgKZiwwNkjEkIIIYQQvYAEOnFazMXF5C5YiN7bm+h//5vr3trTLMyBFuZ83V34dslUglqpxjlak7WGhzY8hLuLO69Oe5UJ4RM6cvjto2QfvH8pVJbAle/DoIudPSIhhBBCCNFLSKATp8xWU0PenxdhLS0l9r13MYaGkl+6tdVjK2otxwxzZpuZZduW8e7edxkRPIJ/nv1PwrzCOmro7SfrJ/jv1WBwg5s+h8gxzh6REEIIIYToRSTQiVOibDby772P2t27iXrpRTyGDqXWbMXVRU+dxdbi+Ah/jzZfq7i6mHu+v4fU4lSuHnQ1S8YuwWgwduTw28fOj2DVnyCwH1zzPwiIdfaIhBBCCCFELyOBTpySkmXPUbF2LSH33ovPeedRa7byx3e3UWexYTToMFub9qHzMBq4Z8bAVl/nl8JfuOf7e6i2VPPU5Ke4qN9FnfUWTp1S8MMz8N1jEDcZrngXPAKcPSohhBBCCNELSaATJ630k2QOvf46/ldcQeCNNzSGuR/3l/CPS0bg6qLn6TX7yC+tIcLfg3tmDGROYmSz11BK8faet3k+9XmifaJ5Y/obJAQkOOkdnQSrGT67A2Tqj/IAACAASURBVNLegxFXwuwXwcXV2aMSQgghhBC9lAQ6cVKqNm2m4OGH8Zo0ibAHH6DOYmsMc0/NG8HlZ0QDtAhwjirqK/jrhr+yLmcd02Kn8cikR/B29e6st3Dqasvgo+shcz2cfS9MvR/a6NQphBBCCCFEZ5BAJ05YXeYB8m6/HdfYWCKfW0Yd+lbD3LH8fuR37lp/F3kVeSwZu4Trh1zf5vYFXUppLqy4HA7+DkkvQ+I1zh6REEIIIYQQEujEibEcOULuggXoDAaiX30Fs4fXSYe5TzM+5ZGftWrcmzPeZExoN+kImb9d22POXA3XfgL9pjp7REIIIYQQQgAS6MQJsNXXk7d4MZbCQmLefhtbaDi3vruNH37X1swdL8zVW+v5xy//4MN9HzI6ZDTPnP0MwZ7BnTT60/T7WvjfjeAZCNetgdAhzh6REEIIIYQQjSTQiWNSSlH4179Ss3UbEc88g374CG59dxvf/17CU5cMbzXMfZ75Oc+nPk9hVSHBHsG4GlzJq8zjxqE3ctvo2zDqu8GWBAC/vAlfLIGw4XD1R+DTDfbFE0IIIYQQvYoEOnFMh159lbKU1QQtXoTbjAtY8F5TmLvijJgWx3+e+TlLNy6l1loLQHFNMQDXDL6Gu8fe3aljP2U2G3zzMGx8AfrPgEuXg1s3aNoihBBCCCF6Hb2zByC6rvIvvqDkuefxnT0L7z/cyoL3trF+XwlPzms9zAE8n/p8Y5hz9G3Otx093PZhroGPb9LC3Bm3wJUrJMwJIYQQQoguSyp0olXVaWnk33c/HmPGEPjw3/jT+6mNYe7Kca2HOYDCqsKTur9LqToE/70KcjfD9Mdg4iLZlkAIIYQQQnRpEuhEC/V5eeT9eREuoaGEPPccf/poF9/tK+GJ44S5kuoSXPQumG3mFo+FeXXx9WeHMuD9S6E8Hy77Dwyd4+wRCSGEEEIIcVwS6EQz1ooKchcsQFkshP7rX/z5s8zGMHfVMcLcr4d+ZfG3i0GBUW9sFurcDe7cPvr2zhj+qcnZBB9cpVXjbvgUosc5e0RCCCGEEEKcEFlDJxopiwXTHXdSn5VN6LJl3LbhCN/tK+HxuccOc+ty1nHDVzcAsGLmCh4981HCvcLRoSPcK5ylk5Zycb+LO+ttnJzdyfCf2eARALd8I2FOCCGEEEJ0K1KhE4B9e4LHHqNqwwaCli7lrv1GvttXzONzh3P1+NbDnFKKt/a8xXPbnmNon6G8cO4LBHsGMyhwUNcNcA2Ugg3Pa90sYyZqzU88A509KiGEEEIIIU6KBDoBwJF33qH0vx/iN38+99b249vfivn73GFthjmz1cwjmx5hVfoqZsTN4LEzH8Pdxb2TR32KrBb48h7YuhyGXQJJL4Oxm4xdCCGEEEIIBxLoBBXffkfRk0/hef75/DV4SmOYu2Z8bKvHH6k9wp3r72Rb0TYWjFzAwpEL0eu6yezdugr4302Q/jWcdSec+xDou8nYhRBCCCGEOIoEul6u9tdfMS1ZgtuQITw+8grW7TvIY3PaDnOZpZn8ed2fKa4u5snJT3b9qZWOyvNhxeVQtBdmPgdjb3L2iIQQQgghhDgtEuh6MXNRMbkLFqL38eH5c29lTUYZj80ZxrUTWg9zG/M3smT9EowGI2/OeJNRIaM6ecSnoXC3FuZqy+Dqj6D/+c4ekRBCCCGEEKdN5pr1UrbqavIWLsRaUcFbFy8mJbeeR48R5j787UP+9M2fCPUK5YOLP+heYS59HSy/QGuEMv8rCXNCCCGEEKLHkApdL6SsVkz3/B+1v/1G8iV38MFhdx6dM4zrWglzFpuFp395mhW/rWBK1BT+MeUfeBm9nDDqU5T6Lnx2BwQP0ipzfpHOHpEQQgghhBDtRgJdL1T8z2epXLeOb8+/ltfNETyaNLTVMFdRX8E9P9zDBtMGrhtyHXePuRuD3uCEEZ8CpeDbx+DHZyD+PLjsbXD3dfaohBBCCCGEaFcS6HqZIx99xOHly9k++jye9hqphbmJcS2Oy6vIY9G6RWSXZ/PQxIe4bMBlnT/YU2Wpg5Q/w67/wejr4eJnwWB09qiEEEIIIYRodxLoepGqjRspfORRDvQbwQNR03lkzrBWw1xqUSp3fHcHFmXhlWmvMD58fOcP9lRVH4YPr4XsDXDeQ3DWXaDTOXtUQgghhBBCdAgJdL1EXUYGebffQUlAGEsGX87SuSO4vpUw92nGpzy88WEivCN46dyXiPNreUyXdfgAvH8ZlGbDJW/C8EudPSIhhBBCCCE6lAS6XsBy+DA5ty6gwqpjSeL13HfpmBZhzqZsvJT2Eq/vep1xYeN4duqz+Ln5OWfApyJvm7Ytgc0C16dA7CRnj0gIIYQQQogOJ4Guh7PV1ZH7pz9TW1jEA5MWsOiqyS3CXLW5mgc3PMjX2V9zSf9LeGD8Axi705qzXz+FT/4APqFwzccQ1N/ZIxJCCCGEEKJTSKDrwZRSmP7yALXbt/PUGddx9Y0XtQhzRVVFLP52Mb8d/o0lY5dw/ZDr0XWnNWc/vwxr/gKRY+Cq/4J3sLNHJIQQQgghRKeRQNeDFb34Lyo//5y3B1/ItIVXc8OkuGaP7z20l8XrFlNpruSFc1/g/9u77/CoqvyP4++TQgq9SgmaIEiPCSCCFBGVIoLAsojSdxV/KgioKLqrgBUQGyIKq0gRRUSqsCsKuIq6QiChi1QhEKSGEhJS5vz+mBApCQlpNzN+Xs+TZ2bOnXvv9+bcSe53TrltqrdxJM5ccaW5E7mf34e6naH7v8A/yOmoREREREQKlRI6L3Vs4WJOTH6Xr6s3oe4Tgy9L5r757RueXf0sZQLKMLPjTGqXq+1MoLmRnABfPADbl0HzwXDni+Dj43RUIiIiIiKFTgmdFzq1NoqD//gHW8vXoPQ/n6N/yxoZy6y1fLj5Q95e/zbhFcJ5u+3bVAiq4GC0V+n07/DpvRC3ATq+BjcPcjoiERERERHHKKHzMmf3/saOhx7heGAZkp9/hf633pCxLDktmTE/jWHxrsV0DOvIC7e8QKBfoIPRXqXDv7hvS3D2KPT6BGp3dDoiERERERFHKaHzIkkn4onu/Tf8U1I5Ovp1+ra/MWPZ8aTjDF81nPWH1/NIxCP8X/j/edbkJ3u+gzl9wD8QBi6DqpFORyQiIiIi4jgldF4iOekc/73vAaoc/51fR7zCfX9plbFsV/wuHl3xKEcTj/Ja69foENbBwUhzYcMcWDQYyl8PvT+HMtc6HZGIiIiISJGQo5kkjDEdjDHbjTE7jTEjM1ne2hiz3hiTaozpccmyNGNMTPrP4vwKXP6QnJrGogHDuHbvFn4b8Bg9/tYlY9kPB36gz7I+JKUmMa39NM9K5qyFb8fBgofguubwt6+UzImIiIiIXCDbFjpjjC/wLnAnEAusNcYsttZuveBt+4ABwJOZbCLRWhuRD7FKJlLSXEwf+jKtYr4l9q576fz0H5OEfLLtE8atHUfNMjWZ1HYSVUpUcTDSq5SaDF8Og5jZcON90Hki+BVzOioRERERkSIlJ10umwI7rbW7AYwxc4B7gIyEzlq7N32ZqwBilCykpLl4a/S/uGvFHI42bskdE54HINWVyrg145izfQ5tQtowtvVYivsXdzjaq5AYD3P7wZ7/Qptn4NanwZPG+4mIiIiIFJKcJHTVgP0XvI4Fbr6KfQQaY6KAVGCstXbhpW8wxgwCBgFce6261OVESpqLF9+YT7f575IQVosWH76D8fHhVPIpRvx3BD8e/JEB9QcwrNEwfH18nQ435+L3weyecGwHdH0PIu53OiIRERERkSIrJwldZk0j9ir2ca219qAxpgaw0hizyVq766KNWTsVmArQpEmTq9n2n1JKmotnp37DPbNfg9JlaDTzA3wCA9l/aj+PrnyU/af2M+aWMXSv1d3pUK/OwRj4pCekJEGf+VDjVqcjEhEREREp0nKS0MUC1S94HQIczOkOrLUH0x93G2O+BSKBXVdcSbKUkubiyZk/cef0cZQmhVofzcKvYkXW/b6OYauGYbFMbTeVmyrf5HSoV2f7f2De3yC4HPRbBJXqOh2RiIiIiEiRl5NZLtcCtYwxYcaYYkAvIEezVRpjyhpjAtKfVwBacMHYO7k6KWkuhn+ynogZb1Dj1CHC3nmbwNo3sHDnQh5Y/gBlAsow+67ZnpfMrfkXzLkPKtSCB1YomRMRERERyaFsW+istanGmMHAV4AvMM1au8UY8wIQZa1dbIy5CVgAlAU6G2PGWGvrA3WBKemTpfjgHkOnhC4XUtJcDJsTQ9U5/6LZoa1c89w/CW7ZgrfWvcWHmz/k5io38/qtr1M6oLTToeacywXfPA8/vgM3dIQeH0IxD5q8RURERETEYTm6sbi1dhmw7JKy5y94vhZ3V8xL1/sRaJjHGP/0UtOTORZ/Qfdd31G2b18Ce3bj8W8fZ8W+Ffz1hr/yzM3P4O/j73SoOZeSCPMHwbbF0HQQdBgLnjR5i4iIiIhIEZCjhE6ck5rmYuicGOK++ZaXNi2kxK23Yof0Z8B/BrD9xHaevulpetftjfGkaf0TjsKnvSA2Ctq/As0e0W0JRERERERyQQldEZaa5mLoZzFs+iGad6NnE1irFqf+8QBD/tOXs6lneaftO7QOae10mFfn6E6Y3QNOx0HPmVCvi9MRiYiIiIh4LCV0RdT5ZO6HNb/y0YaZBJQMZs8/72Pkdw9TLrAcszrOolbZWk6HeXV++8k9+Ynxhf5fQnUPm7xFRERERKSIycksl1LIUtNcDPsshq+j9zFl+xyCEk4RNfxOhm19iTrl6vBJp088L5nb/AXM7ALB5eGBr5XMiYiIiIjkAyV0Rcz5ZG7phgP8K24ZpfZsZ9XAcF49/TmdanTig/YfUD6ovNNh5py1sPpN9z3mqjWBv38N5Wo4HZWIiIiIiFdQl8siJDXNxfC5G/hyYxzvp6yjUtT3rLy7Gu+XWc/giMEMCh/kGZOfbJwLK16Ak7FQLBiSE6BBD+g6GfwCnI5ORERERMRrKKErIs4nc0s2HOTNMge4bvpn/K9xCT668QQTWk2gfWh7p0PMmY1zYclj7tsSgDuZ8/GDWu2UzImIiIiI5DN1uSwCUtNcPJ6ezL1yfSq1P57ItlBfPulUgukdZ3hOMgewYswfydx5rlRY+aIz8YiIiIiIeDG10DnsfDK3eMNBRkWWpOFrwzhYKo0FA+sy6+7JVC5e2ekQc+bwNlg/y93NMjNZlYuIiIiISK4poXNQapqLJz53J3NPt6xE/QmPcjYtkf8+1pIp3d8m2D/Y6RCv7Nxp2Dwf1s+EA1Hg4w9+QZCaePl7S4cUfnwiIiIiIl5OCZ1Dzidzi2IOMuy2a6jxzqMEHjnN+mc7M+besfiYItob1lrY/7O7NW7LAkhJgIp1oN3LcGMv2LXy4jF0AP5BcPvzzsUsIiIiIuKllNA54MJkbtBtJSk76xGu3XmauCd6MqD3GKfDy9yZI7DhU4ieBUd/hWIloOFfILIfhDSB87Nvhvd0P56f5bJ0iDuZO18uIiIiIiL5RgldIUtz2YxkrvetKbiWDKdp1FmS+3Wl7YNFLJlLS4VdK9xdKn/9j3tyk+o3Q5dJUL8bBJTIfL3wnkrgREREREQKgRK6QpTmsjw+N4ZFMQfp3GI/R7+fzLBvUvBr14Y6z7zidHh/OL4boj+GmE/gdBwEV4BmD0NkX6hY2+noREREREQknRK6QpLmsjwxN4ZFMbG0bPoz+zcv5MUllmI3NiTstbecv2F4SiJsW+Jujdv7PRgfqHkHdBwPN3QAv2LOxiciIiIiIpdRQlcIzidzCzfsoUHkl8TGruHNBf4EVirPdZPfwyfAwRtuH4xxj4vb9DkknYSyodD2n3Dj/VC6mnNxiYiIiIhItpTQFbA0l+XJzzewaPNWQhvO4eip35i8tDzBrrNcO+V9/MqXL/ygEk/ApnmwfgYc2gS+AVCvCzTqB9e1BJ8iOsOmiIiIiIhcRAldAcpI5rb9j4q1Z5PiSmHK97UpFruNalOmEFCzZuEF43K5u1JGz4KtiyHtHFQOh7smQMMeEFS28GIREREREZF8oYSugKS5LCM+38CSnf+mVNg8ygVX5O2YcFxrllB59GhKtGxROIGcPOCe3CTmYzixFwJKQ6O+7glOqkYUTgwiIiIiIlIglNAVgDSX5cm5MSyNnUlQyDeEV4rklUMtOP35W5QbMICyve4t2ABSk923GYieBTu/AeuC0FZw2z+gbmf3jb5FRERERMTjKaHLZ2kuy+Ofr+WrwxMJqLiBLtd34cmUthx67TFKtG1LpRFPFtzOj2x3z1K5YQ6cPQolq0DLxyGyN5SrUXD7FRERERERRyihy0dpLstjn3/HquPj8S+9j6GNhtLbvyX77u9NQO3aVHttPMbXN393eu4MbFngTuRi14CPn/s2A436wfW3g6+qWERERETEW+lqP5+kuSwPz13KD2fGUaz4WSbc+iZtgm9kT8978SlRgurvTcanePH82Zm1ELvWncRtWQDJZ6B8LbjzRbixF5SolD/7ERERERGRIk0JXT5Ic1kGfjaD9YnvUCKgBNPumknd4Br81q8/afHxXPfxLPwrV877jhKOurtTRs+CI7+AfzDU7+6e5KT6zeD0zclFRERERKRQKaHLo9Q0F70+G88vyZ9QITCMz+6ZSqWgihwYNpykzZsJmfQOQfXr534HrjTYtdLdGrf93+BKgZCboPNEaNAdAkrm38GIiIiIiIhHUUKXB0mpyXSb8ySxaasIC7qZz7pPJNg/mMNvvMnp5cup9NRTlLz99txt/MReiP7YfcuBUwcguDw0HeRujatUN1+PQ0REREREPJMSulw6nhhPt3kPcdy1lfAS3ZnVfRQ+xof4L+ZzbOpUyvTsSbmBA65uoylJ8MuX7ta4Pf8FDNS8Hdq/ArXvAr9iBXEoIiIiIiLioZTQ5cLu+D3ct3gQCWlHuLXcEN69ZxAACf/7mbhRoyh+S3MqP/dPTE7HtMVtdI+L2zgXkuKh9LXQ5lmIuB/KVC/AIxFvlZKSQmxsLElJSU6HIiIifzKBgYGEhITg7+/vdCgifwpK6LKxdPdS3l7/NocSDlG5eGXuCuvEzM2fci4V7r5mDOPuvgeAc7v3EDt0KMWuu45qb72Fye6PWGI8bJ7nbo2L2wC+xdw3/Y7sC2G3go9PIRydeKvY2FhKlixJaGhozr9YEBERySNrLceOHSM2NpawsDCnwxH5U1BCdwVLdy/ludWjSLHnAIhLiOPDTR/gSi1Jz2pjGdWxNQCpJ06w///+D+PrS/Up7+NbqlTmG7QW9q52t8ZtXQSpSXBNA+gwDsJ7QnC5wjo08XJJSUlK5kREpNAZYyhfvjxHjhxxOhSRPw0ldFfw6v/eyEjmMhgI9Pfj+Q6tAHAlJxM7ZAiphw5x7fTpFAsJuXxDp+Jgwyewfhac2AMBpdzdKSP7QtVI3W5ACoSSORERcYL+/4gULiV0V3Ay+TBk8jcp2Z7AGIO1lkPPPU9i1DqqTphAcKPIP96UlgK/fuVujduxHKwLrmsBbUZC3S5QLLjwDkRERERERLySBmpdgSulzBXLj02ZwslFi6gwZDCl7+7kXnh0Byx/Dt6oB5/1hoPR0GIoDFkPA5fBjb2UzEmRszD6AC3GriRs5FJajF3JwugDTockWdk4F95sAKPLuB83znU6IrlKS3cvpd28doTPCKfdvHYs3b3U6ZAkF1IOH2Zvn76k5lPXwr1799KgQYN82dalvv32W+6++24AFi9ezNixY3O9rdDQUBo2bEhERARNmjTJrxBFJA+U0F1BcEJnrOviyU2sy5/ghM6cWraMI2+9TakunanwQH+Ing3TOsCkJvDTuxDSBO6bA8O3wh2jofz1jhyDSHYWRh/gmfmbOBCfiAUOxCfyzPxNBZ7U3XXXXcTHxxMfH8/kyZMzyi+88ChKRo8eTbVq1YiIiCAiIoJly5YVfhAb58KSx+DkfsC6H5c8VuBJnafV1eeff079+vXx8fEhKirqomWvvvoqNWvWpHbt2nz11VeFHtvS3UsZ/eNo4hLisFjiEuIY/ePoAk/qPK0Or/R5c7oOzzs6+T0S163jyOT3HIshN7p06cLIkSPztI1Vq1YRExNz2edLRJyhLpdX8I9be/Ps8lRMuX9j/OOxKWWwxzvyYlgkB0c+SVD9G6jS7Czm9TqQfBrKXe9O3m68D0pWdjp8EQDGLNnC1oOnslwevS+e5DTXRWWJKWk8NW8jn67Zl+k69aqWYlTn+nmK6/wF2t69e5k8eTKPPPJInraXG6mpqfj55fzP4PDhw3nyyScLLqB/j4RDm7JeHrsW0i4Z15uSCIsGw7oZma9TuSF0zP238eB5ddWgQQPmz5/PQw89dFH51q1bmTNnDlu2bOHgwYPccccd/Prrr/j6+uZbnOPWjOOX479kuXzjkY0ku5IvKktKS+L5H55n3q/zMl2nTrk6PN306TzF5Wl1CJl/3gqjDg+98grntmVdhwA2OZnEjRvBWuLnzOHctm1XnN06oG4dKj/7bLb7Tk1NpX///kRHR3PDDTcwc+ZMJkyYwJIlS0hMTOSWW25hypQpGGOYOHEi77//Pn5+ftSrV485c+aQkJDAkCFD2LRpE6mpqYwePZp77rnnon1Mnz6dqKgoJk2axIABAyhVqhRRUVEcOnSI8ePH06NHDwBee+015s6dy7lz5+jWrRtjxozJwW9PRJygFror6BpZjRl1y7PiQBwb9+xjxYE4PqmQQo0JT+MXmEpIre/w2TrPfbuBgf+GIeug5XAlc+JRLk3msivPqfHjxzNx4kTAfWHWtm1bAFasWEGfPn0IDQ3l6NGjjBw5kl27dhEREcGIESMAOHPmDD169KBOnTr07t0ba22W+wkNDWXUqFE0atSIhg0b8ssv7gux48eP07VrV8LDw2nWrBkbN24E3N/8Dxo0iHbt2tGvXz+mT59O165d6dy5M2FhYUyaNIk33niDyMhImjVrxvHjx/P0e8hXlyZz2ZXnkLfVVd26daldu/Zl+1+0aBG9evUiICCAsLAwatasyZo1a/L0u7talyZz2ZXnlLfVYVaKQh0CJB88ePHrA/nTo2H79u0MGjSIjRs3UqpUKSZPnszgwYNZu3YtmzdvJjExkS+//BKAsWPHEh0dzcaNG3n//fcBePnll2nbti1r165l1apVjBgxgoSEhCvuMy4ujtWrV/Pll19mtNwtX76cHTt2sGbNGmJiYli3bh3fffcd4J7wpF27djRu3JipU6fmy3GLSN6ohe5KNs7lpk2jgEQwUDH5KGenziIl0ZfqvSvhd8cT0OAvEFja6UhFspRdS1qLsSs5EJ94WXm1MkF89lDzXO+3devWvP766zz22GNERUVx7tw5UlJSWL16Na1atWL16tWA+6Jk8+bNxMTEAO4uYNHR0WzZsoWqVavSokULfvjhB1q2bJnlvipUqMD69euZPHkyEyZM4IMPPmDUqFFERkaycOFCVq5cSb9+/TL2sW7dOlavXk1QUBDTp09n8+bNREdHk5SURM2aNRk3bhzR0dEMHz6cmTNnMmzYMAAmTZrEzJkzadKkCa+//jply5bN9e8nU9m1pL3ZIL275SVKV4eBue+y5411lZkDBw7QrFmzjNchISEcyKcL8fOya0lrN68dcQlxl5VXKV6Fjzp8lOv9emMdZvZ5K4w6zK4lLeXwYXbd2c59KyIAa3GdOkW1N17Hr2LFPO27evXqtGjRAoA+ffowceJEwsLCGD9+PGfPnuX48ePUr1+fzp07Ex4eTu/evenatStdu3YF3InY4sWLmTBhAuC+hc2+fZn3tDiva9eu+Pj4UK9ePX7//feM7SxfvpzISPdkb2fOnGHHjh20bt2aH374gapVq3L48GHuvPNO6tSpQ+vWrfN03CKSN2qhu5IVL0BKIimJPuxdUZ7935fl3Ck/Qu6wBIz8Hpr8TcmceLwR7WsT5H9xd6Ugf19GtL+8heNqNG7cmHXr1nH69GkCAgJo3rw5UVFRfP/997Rq1eqK6zZt2pSQkBB8fHyIiIhg7969V3x/9+7dM/Z5/r2rV6+mb9++ALRt25Zjx45x8uRJwD2GJCgoKGP92267jZIlS1KxYkVKly5N586dAWjYsGHG9h5++GF27dpFTEwMVapU4YknnrjaX0ne3f48+AddXOYf5C7PA2+rq6xk1vJU2NOrD200lEDfwIvKAn0DGdpoaJ626211mNXnrSjU4dHJ72FdF/dgsC5Xvoylu/RYjDE88sgjzJs3j02bNvHggw+SlJQEwNKlS3n00UdZt24djRs3JjU1FWstX3zxBTExMcTExLBv3z7q1q17xX0GBAT8cRzpv19rLc8880zGdnbu3Mnf//53AKpWrQpApUqV6NatmyMtpCJyMSV0V3IyFoCjm0uQeKQYiUcCqdzkJMVLH3Y4MJH80zWyGq92b0i1MkEY3C1zr3ZvSNfIannarr+/P6GhoXz00UfccssttGrVilWrVrFr166rusDw9fUlNTU1R++/8L1XuvArXrx4lvvz8fHJeO3j45OxvWuuuQZfX198fHx48MEHnbmICe8JnSe6W+Qw7sfOE93leeBtdZWVkJAQ9u//o4UzNjY24+K0sHSq0YnRt4ymSvEqGAxVildh9C2j6VSjU5626211mNXnrSjUYWJMDKSkXFyYkkJidHSet71v3z5++uknAD799NOMltIKFSpw5swZ5s1zj7N0uVzs37+f2267jfHjxxMfH8+ZM2do374977zzTkZ9ROcypvbt2zNt2jTOnDkDuFu3Dx8+TEJCAqdPnwYgISGB5cuXF9jMnCKSc+pyeSWlQ0g5dID4PcUBA8ZSsmoSlM7k5uEiHqxrZLU8J3CZad26NRMmTGDatGk0bNiQxx9/nMaNG1/0LXTJkiUzLhDye9+zZ8/mueeen/rn0AAACahJREFU49tvv6VChQqUKlUq19uLi4ujSpUqACxYsMC5i5jwnnlO4DLjTXWVlS5dunD//ffz+OOPc/DgQXbs2EHTpk3zfT/Z6VSjU54TuMx4Ux1m9XkrCnVYY+GCAtt23bp1mTFjBg899BC1atXi4Ycf5sSJEzRs2JDQ0FBuuukmANLS0ujTpw8nT57EWsvw4cMpU6YMzz33HMOGDSM8PBxrLaGhoRlj7q5Gu3bt2LZtG82bu7vdlyhRgo8//pgzZ87QrVs3wD2By/3330+HDh3y7xcgIrmihO5Kbn+eo/+8YDyEgSPbylCld966N4n8WbRq1YqXX36Z5s2bU7x4cQIDAy/r/lW+fHlatGhBgwYN6NixI5065c+F7ujRoxk4cCDh4eEEBwczY0YWs0Dm0FNPPUVMTAzGGEJDQ5kyZUq+xFlUeFNdLViwgCFDhnDkyBE6depEREQEX331FfXr16dnz57Uq1cPPz8/3n333XydHdFp3lSHWX3evLkOQ0ND2bp162XlL730Ei+99NJl5efHRV4oKCgo079Nbdq0oU2bNgAMGDCAAQMGAO4ZLy90vkUOYOjQoQwdenlX4A0bNlzpMETEAeZKs1k5oUmTJrao3Nck5fBhdt1+Ozblj+4nppgfNVeszPPAZ5GCtG3btmy7WYmIiBQU/R8SyRtjzDprbZOcvDdHY+iMMR2MMduNMTuNMZfdjdIY09oYs94Yk2qM6XHJsv7GmB3pP/1zdghFw9HJ72G5eICytcbjbiIqIiIiIiLeKdsul8YYX+Bd4E4gFlhrjFlsrb2wX8A+YADw5CXrlgNGAU0AC6xLX/dE/oRfsApy4LOIXJ1u3bqxZ8+ei8rGjRtH+/btHYpIsqK68nyqQxERz5GTMXRNgZ3W2t0Axpg5wD1ARkJnrd2bvuzSOxG3B7621h5PX/410AH4NM+RF4KCHPgsUtCstYU+nXdBWrBAn0dPobryfKpDyYuiNpxHxNvlpMtlNeDCO9nGppflRI7WNcYMMsZEGWOijhw5ksNNi0hWAgMDOXbsmP6piohIobLWcuzYMQIDA7N/s4jki5y00GX2FX9OrxJztK61diowFdyTouRw2yKShZCQEGJjY9EXJCIiUtgCAwMJCdEtnkQKS04Sulig+gWvQ4CDOdx+LNDmknW/zeG6IpJL/v7+hIWFOR2GiIiIiBSwnHS5XAvUMsaEGWOKAb2AxTnc/ldAO2NMWWNMWaBdepmIiIiIiIjkUbYJnbU2FRiMOxHbBsy11m4xxrxgjOkCYIy5yRgTC/wVmGKM2ZK+7nHgRdxJ4VrghfMTpIiIiIiIiEje6MbiIiIiIiIiRcjV3Fi8yCV0xpgjwG9Ox5GJCsBRp4MQr6ZzTAqSzi8pSDq/pCDp/JKCVFTPr+ustRVz8sYil9AVVcaYqJxmySK5oXNMCpLOLylIOr+kIOn8koLkDedXTiZFERERERERkSJICZ2IiIiIiIiHUkKXc1OdDkC8ns4xKUg6v6Qg6fySgqTzSwqSx59fGkMnIiIiIiLiodRCJyIiIiIi4qGU0ImIiIiIiHgoJXQ5YIzpYIzZbozZaYwZ6XQ84j2MMdWNMauMMduMMVuMMUOdjkm8jzHG1xgTbYz50ulYxPsYY8oYY+YZY35J/1vW3OmYxHsYY4an/3/cbIz51BgT6HRM4rmMMdOMMYeNMZsvKCtnjPnaGLMj/bGskzHmhhK6bBhjfIF3gY5APeA+Y0w9Z6MSL5IKPGGtrQs0Ax7V+SUFYCiwzekgxGu9DfzHWlsHuBGda5JPjDHVgMeAJtbaBoAv0MvZqMTDTQc6XFI2Elhhra0FrEh/7VGU0GWvKbDTWrvbWpsMzAHucTgm8RLW2jhr7fr056dxXwhVczYq8SbGmBCgE/CB07GI9zHGlAJaAx8CWGuTrbXxzkYlXsYPCDLG+AHBwEGH4xEPZq39Djh+SfE9wIz05zOAroUaVD5QQpe9asD+C17HogtuKQDGmFAgEvjZ2UjEy7wFPAW4nA5EvFIN4AjwUXq33g+MMcWdDkq8g7X2ADAB2AfEASettcudjUq80DXW2jhwf9EOVHI4nqumhC57JpMy3etB8pUxpgTwBTDMWnvK6XjEOxhj7gYOW2vXOR2LeC0/oBHwnrU2EkjAA7srSdGUPpbpHiAMqAoUN8b0cTYqkaJHCV32YoHqF7wOQc39ko+MMf64k7nZ1tr5TscjXqUF0MUYsxd3d/G2xpiPnQ1JvEwsEGutPd+zYB7uBE8kP9wB7LHWHrHWpgDzgVscjkm8z+/GmCoA6Y+HHY7nqimhy95aoJYxJswYUwz3YNzFDsckXsIYY3CPPdlmrX3D6XjEu1hrn7HWhlhrQ3H/7VpprdW325JvrLWHgP3GmNrpRbcDWx0MSbzLPqCZMSY4/f/l7WjSHcl/i4H+6c/7A4scjCVX/JwOoKiz1qYaYwYDX+GeXWmatXaLw2GJ92gB9AU2GWNi0suetdYuczAmEZGrMQSYnf6l525goMPxiJew1v5sjJkHrMc9K3Q0MNXZqMSTGWM+BdoAFYwxscAoYCww1xjzd9xfIvzVuQhzx1ir4WAiIiIiIiKeSF0uRUREREREPJQSOhEREREREQ+lhE5ERERERMRDKaETERERERHxUEroREREREREPJQSOhER8VrGmDRjTMwFPyPzcduhxpjN+bU9ERGR3NB96ERExJslWmsjnA5CRESkoKiFTkRE/nSMMXuNMeOMMWvSf2qml19njFlhjNmY/nhtevk1xpgFxpgN6T+3pG/K1xjzL2PMFmPMcmNMkGMHJSIif0pK6ERExJsFXdLl8t4Llp2y1jYFJgFvpZdNAmZaa8OB2cDE9PKJwH+ttTcCjYAt6eW1gHettfWBeOAvBXw8IiIiFzHWWqdjEBERKRDGmDPW2hKZlO8F2lprdxtj/IFD1tryxpijQBVrbUp6eZy1toIx5ggQYq09d8E2QoGvrbW10l8/Dfhba18q+CMTERFxUwudiIj8Wdksnmf1nsycu+B5GhqbLiIihUwJnYiI/Fnde8HjT+nPfwR6pT/vDaxOf74CeBjAGONrjClVWEGKiIhcib5JFBERbxZkjIm54PV/rLXnb10QYIz5GfeXm/ellz0GTDPGjACOAAPTy4cCU40xf8fdEvcwEFfg0YuIiGRDY+hERORPJ30MXRNr7VGnYxEREckLdbkUERERERHxUGqhExERERER8VBqoRMREREREfFQSuhEREREREQ8lBI6ERERERERD6WETkRERERExEMpoRMREREREfFQ/w+bwq2UfmSacQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
